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PhD Research Intern, Quantum and AI for Chemistry...
NVIDIA’s Quantum and Chemistry Research team is seeking Molecular AI and Quantum Research Interns to work in our newly-formed Quantum and Chemistry Research team led by Alán Aspuru-Guzik. The Quantum and Chemistry Research team explores novel approaches to integrate world-class NVIDIA multi GPU architectures with state-of-the-art and near-term quantum computers. We also study how to develop new AI models for chemistry as well as agentic systems for computational chemistry. We are interested in robotics for self-driving labs as well. The Quantum and Chemistry Research Scientist will help develop either of: Quantum algorithms at the interface of artificial intelligence and many-body physics models such as quantum chemistry. Generative models for chemistry. Self-driving lab robotics capabilities. Agentic AI systems for computational chemistry. What You'll Be Doing: Advance the state-of-the-art of AI-driven quantum algorithms, generative models for chemistry, self-driving labs, or agentic AI. Present at relevant scientific, technical and industry conferences. What We Need To See: Pursuing a PhD Degree in Chemistry, Physics, Computer Science, Applied Mathematics, Engineering, or related engineering field. Background in Quantum Information Science, or AI for chemistry with expertise in one of the following: quantum chemistry, quantum machine learning, or applied quantum algorithms, robotics for self-driving labs, generative models for chemistry, or computational agentic frameworks. Research experience in a graduate setting in any of the topics above. World-class communication skills with a demonstrated ability to articulate a value proposition to technical and non-technical audiences. Ways To Stand Out From The Crowd: Background with GPU computing. Experience in software systems development. Experience in Machine Learning methods. Experience scaling or optimizing large-scale AI models. Hands-on experience in scientific computing, high-performance computing, applied machine learning, or deep learning. NVIDIA's invention of the GPU revolutionized parallel computing. Our GPUs are being used in many of the largest high-performance computing projects around the world, solving real world problems, including advancing the state of Quantum Information Science. NVIDIA is widely considered to be one of high technology's most desirable employers. We have some of the most forward-thinking and experienced people in the world working for us. Our goal is to craft an environment where we can do our life's best work. If you're creative, autonomous, and highly motivated, we want to hear from you! Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The expected hourly rate for this position is 77 CAD – 99 CAD. You will also be eligible for Intern benefits . Applications for this job will be accepted at least until November 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.
Game Development Engineer
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM Our team builds immersive, high-performance wargaming applications for both desktop and augmented reality that transform complex simulation state into intuitive, actionable, workflows that allow the warfighter to learn before the fight. Beyond visualization, our team builds the distributed systems and networking protocols that power these environments, integrating directly with Lattice to bridge synthetic wargaming with live, operational battlespace execution. We are looking for elite C++ engineers who operate from first principles and obsess over the end user experience as much as frame budgets. If you want to build high-consequence software that fundamentally transforms American and allied defense capabilities, this is where you belong. WHAT YOU’LL DO - Design, develop, and ship software that scales wargaming and simulation capabilities internationally. - Partner with stakeholders to establish clear priorities and define expectations which translate to a scope of work that you and the team can effectively deliver on. - Build intuitive desktop and AR interfaces to visualize complex battlespace data. - Engineer reliable, responsive software designed to run smoothly across diverse hardware targets, from high-end desktop systems to mobile AR devices. - Diagnose and resolve complex defects across clients, backends, and networking layers to ensure mission-critical platform stability. - Work through ambiguous operational requirements with stakeholders, scoping and delivering high-impact technical solutions. REQUIRED QUALIFICATIONS - 5+ years of professional software engineering experience. - Strong proficiency in modern C++ and deep foundational knowledge of software architecture and engineering principles. - Experience shipping production features in video games, real-time simulation, or 3D visualization systems, with an appreciation for low-level systems. - Strong foundation in 3D mathematics (linear algebra, vector math, coordinate transformations). - Proven track record of navigating, architecting, and shipping within large, complex codebases with an eye for clean, extensible, and reusable code. - Strong written and verbal communication skills, with the ability to convey technical concepts and tradeoffs to both technical and operational stakeholders. - Must be a U.S. Person due to required access to U.S. export controlled information or facilities. PREFERRED QUALIFICATIONS - Shipped at least one AAA game title or commercial simulation product in an engineering role. - Experience developing for console platforms or resource-constrained mobile/embedded hardware. - Deep experience with modern game engines (C++ source-level). - Background in networked multiplayer architecture, client-server replication, or distributed backend systems. - Familiarity with Augmented Reality (AR), Virtual Reality (VR), or OpenXR development. - Comfortable with Linux development environments and cross-platform build systems. - Be able to obtain and hold a U.S. Top Secret security clearance. US Salary Range $166,000 - $220,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Urology Clinical Account Executive, East
Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients at the right time. Responsibilities: Drive strategic business expansion/collaboration opportunities with the following: Major Urology groups and clinics / Top 20 largest urology practices in the territory Key Opinion Leaders (KOLs) and Academic Medical Centers (AMCs) within the specified territory. Structure detailed strategic plans for gaining and retaining new and existing clients. Maximize client-bill contracting opportunities Implement laboratory services agreements (LSA’s) with bill account institutions Collaborate and coordinate with all sales positions (VP, Sales, RSD’s, DSM’s, SAM’s, and GL’s) to ensure successful attainment of company goals and objectives Identify and develop partnering opportunities between prospective oncology clients and Tempus. Promote and drive compliance with new web-based molecular information tools for all clients Continually analyze competitive landscape and environment within assigned accounts to determine trends and provide customer feedback to Tempus leadership Monitor performance of sales to ensure objectives are met Develop and implement a comprehensive business plan for the territory that will be inclusive of budgets, travel, territory management, goal setting, etc. Work effectively with individuals across multiple departments throughout Tempus Embrace, embody and represent the Tempus company culture at all times to external and internal constituents Preferred Qualifications: Ability to provide an integrated MolDx/SaaS solution using Tempus’s sequencing technology to prospects and customers. Ability to engage in a consultative selling process that overcomes objections and indifferences while connecting client needs with Tempus’ capabilities. Comfortable selling at the executive level (CEO, COO, CFO) Keen understanding of the payor and reimbursement environment in the oncology and diagnostic space Ability to work independently, communicate proactively, manage multiple projects and prioritize daily tasks while managing critical deadlines Strong understanding of molecular diagnostics for oncology and the evolving competitive landscape #LI-NK1 #LI-Remote $95,000-$155,000 The expected salary range may vary for other locations. Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Senior Software Engineer, Dart
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM The Tactical Recon & Strike team at Anduril develops aerial small drones (Group 1-3) and all equipment to test, deploy and operate. The team is responsible for taking products like Ghost, Anvil, Bolt and Altius from concepts to operational systems. We work in close coordination with specialist teams to solve some of the hardest problems facing our customers. We are looking for software engineers, hardware engineers, roboticists, and front-end mobile developers who are excited about creating a powerful robotics platform that can be tasked and viewed in real-time through a variety of interfaces. ABOUT THE JOB As a Robotics Software Engineer on our TRS team, you'll be at the forefront of cutting-edge autonomous systems development. You'll tackle diverse challenges in autonomy, systems integration, robotics, and networking, making critical engineering decisions that directly impact mission success. Your role will be pivotal in ensuring Anduril's products work seamlessly together to achieve a variety of crucial outcomes. You'll develop innovative solutions for complex robotics problems, balance pragmatic engineering trade-offs with mission-critical requirements, and collaborate across teams to integrate software with hardware systems. Contributing to the entire product lifecycle, from concept to deployment, you'll rapidly prototype and iterate on software solutions. We're looking for someone who thrives in a fast-paced environment and isn't afraid to tackle ambiguous problems. Your "Whatever It Takes" mindset will be key in executing tasks efficiently, scalably, and pragmatically, always keeping the mission at the forefront of your work. This role offers the opportunity to make a significant impact on next-generation defense technology, working with state-of-the-art robotics and autonomous systems. You'll be part of a team that values innovation, quick iteration, and delivering high-quality solutions that meet real-world needs. Must be eligible to obtain and maintain an active U.S. Secret security clearance. This position will be located at our headquarters in Costa Mesa, CA (relocation benefits provided.) WHAT YOU'LL DO - Develop and maintain core robotics libraries, including frame transformations, targeting, and guidance systems, that will be utilized across all Anduril robotics platforms - Lead the development and implementation of major features for our products, such as designing and building an end-to-end target tracking pipeline for Dart - Collaborate closely with hardware and manufacturing teams throughout the product development lifecycle, providing timely feedback to influence and enhance final hardware designs - Troubleshoot and resolve complex issues in deployed systems, ensuring optimal performance in the field - Participate in the full software development lifecycle, from concept and design through testing and deployment - Stay current with emerging technologies and industry trends, recommending and implementing innovations to improve our products and processes REQUIRED QUALIFICATIONS - Bachelor's degree in Robotics, Computer Science, or related field - 5+ years of professional software development experience - Strong proficiency in C++ or Rust, with experience in Linux development environments - Demonstrated expertise in data structures, algorithms, concurrency, and code optimization - Proven experience troubleshooting and analyzing remotely deployed software systems - Hands-on experience working with and testing electrical and mechanical systems - Ability to collaborate effectively with cross-functional teams, including hardware and manufacturing - Strong problem-solving skills and a "Whatever It Takes" mindset - Excellent communication skills, both written and verbal - Eligible to obtain and maintain an active U.S. Secret security clearance - Willingness to relocate to Costa Mesa, CA PREFERRED QUALIFICATIONS - Master's or Ph.D. in a relevant field (e.g., Robotics, Computer Science, Electrical Engineering) - Expertise in one or more advanced robotics areas: motion planning, perception, localization, mapping, or controls - Experience with performance optimization and metrics for complex robotic systems - Proficiency in Python, Rust, and/or Go, in addition to C++ - Hands-on experience programming for embedded systems and physical devices - Background in multi-agent coordination, particularly with UAVs - Demonstrated ability to solve complex frame transformation problems (e.g., target localization, multi-degree-of-freedom robotic arms) - Experience with real-time operating systems and distributed computing - Familiarity with machine learning and AI applications in robotics - Knowledge of sensor fusion techniques and implementation - Understanding of aerodynamics and flight dynamics as applied to UAV systems - Experience with simulation environments for robotics testing and development - Track record of contributions to open-source robotics projects or relevant publications - US Salary Range $220,000 - $292,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
3D Machine Learning Engineer
FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field. What You’ll Do Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding. Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2). Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines. Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM). Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization. What You Have Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, or a related technical field. 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks. Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning. Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar. Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing. Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows. Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production. Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams. The Extras That Set You Apart Experience working with BIM data, digital twins, or construction-related sensor data. Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations. Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow. Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning. Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks). Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D. Experience building custom modules for SparseConvNet or 3D transformers.
Senior AI Infrastructure Engineer
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic systems. The team is responsible for taking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product. The team also develops Lattice for Mission Autonomy, Anduril’s premier software platform that enables masses of Fury, Barracuda, and other first and third party robots to collaborate across various missions. We work in close coordination with specialist teams like Perception, Motion Planning, Hardware, and Test Engineering to solve some of the hardest problems facing our customers. We are looking for software engineers and roboticists excited about creating a powerful autonomy software stack that includes computer vision, motion planning, SLAM, controls, estimation, and secure communications. ABOUT THE JOB We are looking for a Senior AI Infrastructure Engineer to build, scale, and optimize the end-to-end machine learning platform that powers Anduril’s autonomous systems. In this role, you will own critical components of our ML platform and MLOps tooling. You will build and operate the infrastructure required to train, evaluate, host, and serve complex AI models (including LLMs, computer vision, and RL agents) across cloud environments and air-gapped, edge-deployed networks. Working closely with AI Research Scientists and Platform Engineers, you will eliminate friction in model development, optimize hardware utilization, and ensure robust delivery of models into safety-critical operational environments. WHAT YOU’LL DO - Build, optimize, and maintain scalable training, orchestration, and experimentation infrastructure to accelerate state-of-the-art model development. - Identify and resolve bottlenecks in the ML lifecycle by developing tooling for experiment tracking, automated profiling, and hyperparameter tuning. - Implement and scale robust data pipelines (ETL) to process multi-modal data (video feeds, radar, flight telemetry, and simulation logs) captured from physical assets and test sites. - Deploy high-throughput, low-latency model serving frameworks optimized for both cloud environments and resource-constrained, air-gapped tactical edge hardware. - Develop robust CI/CD pipelines for ML models, including automated regression testing, validation benchmarks, and safe rollout/rollback strategies. - Implement pipelines for model evaluation, validation, and reinforcement learning alignment loops (RLHF/DPO) to ensure predictability and safety in mission-critical deployments. - Partner with AI Researchers and Computer Vision engineers to translate modeling requirements into scalable, reusable infrastructure. - Mentor peers, conduct thorough design and code reviews, and champion engineering best practices across the team. REQUIRED QUALIFICATIONS - 5+ years of software engineering experience with demonstrated success in building and operating production-scale machine learning infrastructure or distributed systems. - Proficiency in Python, Go, or C++, with a strong grasp of software engineering fundamentals, systems design, and concurrent programming. - Hands-on experience with container orchestration (Docker, Kubernetes) and distributed training frameworks (e.g., PyTorch Distributed, Ray, Slurm, or Megatron-LM). - Experience building and maintaining distributed data pipelines handling large-scale unstructured or multi-modal datasets. - Track record of owning projects end-to-end—from technical design to production deployment and operational monitoring. - Eligible to obtain and maintain an active U.S. Top Secret security clearance. PREFERRED QUALIFICATIONS - Experience deploying ML infrastructure, model serving, or artifacts in secure, air-gapped, or regulated environments (e.g., IL5/IL6, GovCloud). - Hands-on experience profiling GPU/accelerator workloads, resolving hardware/network bottlenecks, and optimizing compute utilization. - Experience supporting workloads for Large Language Models, Generative AI, or Reinforcement Learning (RL) pipelines. - Experience with multi-tenant cluster management, including fair scheduling, GPU slicing, and quota enforcement. - Familiarity with production ML observability frameworks, including data drift detection and automated evaluation pipelines. US Salary Range $191,000 - $253,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Technical Lead Manager, Prediction, ML Evaluation
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently. We have an exciting opportunity for a Staff Technical Lead Manager to lead our ML Evaluation team. In this role, you will define the strategic vision for our evaluation platforms, scaling the critical infrastructure and metrics required, and partner closely with the modeling teams to rigorously validate our next-generation deep neural networks and accelerate ML developer velocity across PrePlan. You will: - Influence the strategic direction of foundational infrastructure and evaluation platforms to robustly support next-generation ML model evaluation use cases - Collaborate cross-functionally with ML engineers, data scientists, and infrastructure teams to identify, define, and surface critical signals on model, component, and system-level performance - Leverage and scale evaluation and infrastructure platforms to significantly enhance the ML developer experience, enabling faster iteration through earlier, more reliable, and trusted model evaluation - Manage and mentor a focused team of engineers, aligning their career growth and aspirations with critical organizational needs - Drive best practices and leverage deep technical awareness of the Alphabet ML stack (e.g., TensorFlow, JAX, Flax, Apache Beam) to optimize evaluation workflows - Stay at the forefront of emerging technologies, industry trends, and research in ML evaluation methodologies and advanced metrics design You have: - M.S. in Computer Science, Mathematics, or equivalent industry experience in Robotics or large-scale ML systems with critical evaluation needs - 5+ years of experience building and maintaining large-scale distributed infrastructure, ML inference systems, or evaluation platforms, including 3+ years of engineering management experience - Strong coding and testing proficiency, specifically in Python and C++ - Strong foundational knowledge of model evaluation and core data science principles (e.g., confidence intervals, outlier identification, curve fitting, and causality analysis) - Familiarity with large-scale ML deployment and orchestration tools (e.g., TF Serving, TorchServe, Kubeflow, SageMaker Pipelines, or Vertex AI Pipelines) - Understanding of machine learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or TensorFlow We prefer: - Experience developing and maintaining evaluation pipelines for ML models - Experience deploying and supporting machine learning models for computer vision, natural language processing, robotics/motion planning, or recommendation systems - Experience supporting a small team of MLEs developing high-capacity, production-grade models and components - Strong understanding of metrics computation and regression detection at scale The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $251,000 - $310,000 USD
Staff Research Scientist, Perception
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. This role follows a hybrid work schedule and reports to a Principal Research Scientist. You will : - Research & Develop state-of-the-art Multimodal LLMs and World models to perform 3D Perception using sensor information from Camera, LiDAR and Radar. - Integrate emerging research from the broader AI community into Waymo’s Encoders and Sensor understanding models - Partner with engineering and research teams across Waymo to share recipes, techniques, and best practices to accelerate our collective know-how. - Develop and maintain scalable data pipelines for Training & Eval to process data from multiple sources. - Design and implement evaluation frameworks for perception models. - Study and analyze different behaviors of this model, such as scaling efficacy, downstream quality implications, model architecture design ablations, etc. - Design and implement Perception Modeling solutions to understand LiDAR/Camera/Radar information from autonomous vehicle sensors. - Conducting cutting-edge research and potentially communicating research findings to the wider academic community via technical reports and/or publications. You have: - PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field, with 4+ years of industry or post-doc research experience in Reinforcement Learning or Foundation Models. - Demonstration of original contributions to the field through high-impact publications (ArXiv, peer-reviewed conferences like NeurIPS/ICLR/CVPR), technical blog posts, or significant open-source contributions. - Proficiency in implementing model training flows in a scalable, distributed and performant manner such as Data parallel, FSDP and other sharding approaches. - A willingness to work with complexity of globally distributed inference infrastructure. We prefer: - PhD in Computer Science, Machine Learning, or Robotics, with a research focus on Reinforcement Learning, Foundation Models, or Multi-Modal learning. - Extensive experience designing and deploying Reinforcement Learning infrastructure, specifically for on-policy learning or alignment with human preferences. - A consistent history of original contributions to the AI community, evidenced by first-author publications at top-tier venues (e.g., NeurIPS, ICLR, ICRA) or maintaining significant open-source ML projects - Substantial involvement in and contributions to high impact industry AI projects. - Experience in generative models for domains such as world models, images, videos, 3D, using techniques such as diffusion or autoregressive models. In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include: - Health, dental, vision, life, disability insurance - Retirement Benefits: 401(k) with company match - Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment - Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) - Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks - Baby Bonding Leave: 18 weeks - Holidays: 13 paid days per year The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $251,000 - $310,000 USD
Staff Machine Learning Engineer(TLM), Driver Under...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Driver Understanding and Evaluation (DUE) team at Waymo is developing rich metrics for understanding the behavior of the Waymo Driver in the real world, and technologies such as context and scene analysis to understand driving, understanding and augmenting real world driving data to generate rare driving events, build large scale data infrastructure, improve components such as agents and a realistic simulator. These technologies come together to drive the overall technical strategy and methodology used to evaluate the behavior of the Waymo Driver. The DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgements and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques for the Evaluation systems on our autonomous vehicles, and have an incessant drive to improve the performance of our technology stack. You will: - Lead the development of cutting edge Deep Learning and machine learning models to enhance human-led triaging and introduce automation for high-volume workflows - Design and build Gen AI LLM/VLM solutions for self driving car behavior analysis and anomaly detection - Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a Reinforcement Learning from human preference-based data collection and evaluation system. - Enhance User Feedback Analysis, collaborate seamlessly with product and business teams to design and implement tools for multi-label classifications, sentiment assessment, comment summarization, root cause analysis and trend analysis of rider feedback - Oversee the production and optimization of machine learning models aiming to assess Waymo's expansive fleet of vehicles that cumulatively travel millions of miles. - Drive technical direction, and provide technical inputs and guidance to the team. - Work closely with PMs and TPMs to help define product requirements and align the technical agenda with the company's business objectives. - Collaborate closely with multiple teams (e.g., Prediction, Planning, Research), other technical leads, and senior leaderships across Waymo to deliver on key strategic efforts. You have: - B.S. in Computer Science, Robotics, Machine Learning, similar technical field of study, or equivalent practical experience - 7+ years of experience with hands-on experience in machine learning projects - Strong coding experience in C++ and/or Python. - Experience in at least one of: Foundational Models, VLM, Deep Learning - Experience with ML frameworks such as TensorFlow, PyTorch, Hugging Face's transformers, along with expertise in deep learning models and ML deployment at scale We prefer: - M.S. or Ph.D. degree Computer Science or related quantitative field with a specialization of machine learning. - 10+ years of experience with hands-on experience in machine learning projects - Deep learning experience with Transformers - Gen AI LLM/VLM experience - Experience with building tools for applied machine learning, including MLOps, evaluation/validation techniques, and model performance optimization. - Large-scale data processing and analytical skills. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $238,000 - $302,000 USD
Senior Research Scientist, Perception
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. This role follows a hybrid work schedule and reports to a Principal Research Scientist. You will : - Research & Develop state-of-the-art Multimodal LLMs and World models to perform 3D Perception using sensor information from Camera, LiDAR and Radar. - Integrate emerging research from the broader AI community into Waymo’s Encoders and Sensor understanding models - Develop and maintain scalable data pipelines for Training & Eval to process data from multiple sources. - Design and implement evaluation frameworks for perception models. - Study and analyze different behaviors of this model, such as scaling efficacy, downstream quality implications, model architecture design ablations, etc. - Design and implement Perception Modeling solutions to understand LiDAR/Camera/Radar information from autonomous vehicle sensors. - Conducting cutting-edge research and potentially communicating research findings to the wider academic community via technical reports and/or publications. You have: - PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field, with 2+ years of industry or post-doc research experience in Reinforcement Learning or Foundation Models. - Demonstration of original contributions to the field through high-impact publications (ArXiv, peer-reviewed conferences like NeurIPS/ICLR/CVPR), technical blog posts, or significant open-source contributions. - Proficiency in implementing model training flows in a scalable, distributed and performant manner such as Data parallel, FSDP and other sharding approaches. - A willingness to work with complexity of globally distributed inference infrastructure. We prefer: - PhD in Computer Science, Machine Learning, or Robotics, with a research focus on Reinforcement Learning, Foundation Models, or Multi-Modal learning. - Extensive experience designing and deploying Reinforcement Learning infrastructure, specifically for on-policy learning or alignment with human preferences. - A consistent history of original contributions to the AI community, evidenced by first-author publications at top-tier venues (e.g., NeurIPS, ICLR, ICRA) or maintaining significant open-source ML projects - Substantial involvement in and contributions to high impact industry AI projects. - Experience in generative models for domains such as world models, images, videos, 3D, using techniques such as diffusion or autoregressive models. In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include: - Health, dental, vision, life, disability insurance - Retirement Benefits: 401(k) with company match - Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment - Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) - Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks - Baby Bonding Leave: 18 weeks - Holidays: 13 paid days per year The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD
Senior Machine Learning Engineer, Simulation Evalu...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Challenge Waymo’s simulator is one of the most complex virtual environments ever built. It blends deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver. The Simulator Evaluation team faces the ultimate data challenge: How do you mathematically prove that a virtual world is "real" ? We are seeking visionary machine learning engineers and researchers to architect the scalable deep learning systems, novel data workflows, and eval tools that power our research roadmap. In this role, you will pioneer the machine learning and generative vision paradigms required to define and measure the realism of our multimodal world models. Your work will define the state of the art for autonomous simulation, directly steering our research trajectory and the capabilities of the Waymo Driver. You will: - Lead the design, development and deployment of cutting-edge evaluation approaches to assess realism of state-of-the-art multimodel world models and generative systems for simulation use cases at Waymo. - Architect and implement robust and scalable machine learning pipelines for tuning, evaluating, and deploying large-scale discriminator models for the purposes of simulator realism evaluation. - Evaluate open-source and production-ready video generation techniques that measure realism (e.g. temporal stability, multi-modal consistency, geometric discrepancy, condition following, etc.) - Apply vision language models to evaluate semantic understanding and controllability across our world simulation products. - Collaborate with research teams across Waymo and Alphabet to integrate advancements in 4D world modeling and generative AI into production systems. - Mentor engineers on the team and provide technical guidance on architecture and execution. You have: - Bachelor's, Master's, or PhD in computer science, machine learning, robotics, or a related field. - Five or more years of experience in machine learning engineering or applied deep learning, supported by a portfolio of shipped products or peer-reviewed publications. - Proficient programming skills in Python and hands-on experience with modern machine learning frameworks such as Jax, Flax, or PyTorch. - Experience designing and implementing evaluation frameworks for complex systems or machine learning models. We prefer: - Track record of training large-scale generative models (diffusion models, flow matching, vision language models, etc.) - A PhD and demonstrated success delivering machine learning products focused on 3D generative models, world models, or video generation. - Experience simulating sensor data, including camera, lidar, and radar, or modeling semantic scenes. - Experience developing autonomous systems, robotics software, or autonomous vehicle simulations. - Experience training and optimizing large-scale models on GPU or TPU clusters for efficient production serving. - Professional experience writing C++ for high-performance production systems. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD
Senior Machine Learning Engineer, Multimodal Perce...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Special Vehicle Compliance team develops the multi-modal perception, semantic reasoning, and driving intelligence that enables the autonomous vehicle to safely interact with high-stakes road actors. We are actively advancing our systems toward data-driven learned policies and end-to-end architectures, powered by large-scale closed-loop data engines. Role overview: Perception-focused MLE role dedicated to multi-modal sensor fusion, multi-task deep learning architectures for vehicle semantics and signal detection, dynamic high-resolution vision backbones, and large-scale automated data engines. In this hybrid role, you will report to the Technical Lead Manager of the Special Vehicle Compliance team. You will: - Architect, train, and optimize multi-task deep learning models (PyTorch / JAX) across multi-modal sensor streams. - Build automated data mining pipelines, active learning loops, hard-example curation, and auto-labeling systems. - Develop high-resolution vision architectures and spatial-temporal transformer backbones. - Leverage multimodal foundation models for automated data curation, synthetic edge-case generation, and failure triage. - Profile and optimize models for efficient onboard accelerator inference. You have: - 2–5+ years training and deploying production vision or multi-modal deep learning models. - Experience with multi-modal sensor fusion (Camera + LiDAR + Audio), multi-task learning (MTL), transformer architectures, and PyTorch / JAX. - Experience building large-scale data curation pipelines, active learning loops, and auto-labeling systems. - Fluency with modern AI developer tools and foundation model workflows for fast prototyping. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD
2027 Summer Intern, MS/PhD, Software Engineer, Sim...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! You will: - Design and implement an automated semantic extraction pipeline that parses large-scale simulation logs, vehicle trajectory data, and multi-agent behavioral events into structured graph representations (entities, temporal relationships, and causal interactions) - Develop temporal graph modeling techniques to capture time-varying multi-agent interactions, road context, and safety-critical driving events - Build a multi-modal hybrid retrieval engine (combining dense vector embeddings, keyword search, and graph traversal) to enable fast scenario discovery and serve as structured memory for automated failure analysis agents - Create interactive data exploration tools and visualization dashboards (e.g., Jupyter/Colab-based explorers) to help autonomy engineers and researchers analyze complex scenario distribution - Benchmark retrieval precision, recall, and query latency against traditional tabular and relational search baselines - Collaborate cross-functionally with simulation researchers, machine learning engineers, and software infrastructure teams to document system architecture and establish roadmap recommendations You have: - Currently enrolled in a graduate program (PhD or Master’s) in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a related quantitative field, with at least one academic term remaining - Strong software development experience in Python and/or C++ in a Linux development environment - Solid foundation in core computer science concepts, data structures, algorithm complexity, and distributed data systems - Hands-on experience with modern deep learning frameworks (such as PyTorch, JAX, or TensorFlow) We prefer: - Demonstrated research background or practical experience in Knowledge Graphs, Graph Algorithms, Graph Neural Networks (GNNs), or Information Retrieval / Retrieval-Augmented Generation (RAG) - Authorship of published papers in top-tier AI/ML, data mining, or computer vision conferences (e.g., NeurIPS, ICML, ICLR, KDD, The Web Conference [WWW], CVPR, CoRL, SIGMOD, VLDB, ACL) - Experience with large-scale distributed data processing systems (e.g., Apache Spark, Apache Beam, distributed SQL query engines, or columnar data lakes) - Familiarity with temporal graphs, bi-temporal data modeling, or graph databases/tooling - Demonstrated interest or domain experience in autonomous vehicle simulation, behavior prediction, motion planning, trajectory forecasting, or multi-agent interaction modeling General Perks - Help solve challenging problems with a direct impact on the company - Competitive compensation packages with a housing/relocation bonus (if applicable) - Medical, dental, and vision insurance - Fun intern events and networking opportunities Onsite Perks - Free breakfast, lunch, dinner, and snacks - Free access to Google shuttles - Onsite gym Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly Masters Pay $70 - $70 USD The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85 - $85 USD
2027 Summer Intern, MS/PhD, Machine Learning Engin...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! You will: - Train and fine-tune a large multi-task transformer over driving-log sequences, in JAX/Flax on TPUs - iterating on fine-tuning strategies, training data mixtures, and losses to improve evaluation quality for the hillclimbing workflow - Design and run rigorous offline and end-to-end evaluations - PR-AUC, calibration quality, and metric sensitivity on real hillclimbing A/B runs - and build the dataset and evaluation pipelines needed to produce them - Land production-quality code in a shared, high-traffic codebase, and communicate results through a design doc, team deep dives, and a final intern presentation, partnering with UEM Core, Data Science, and release-eval stakeholders You have: - Currently enrolled in an PhD or MS program in Computer Science, Machine Learning or a related field, returning to the program after the internship - Hands-on experience training and evaluating deep learning models in a modern framework (JAX, PyTorch or TensorFlow), including building data pipelines, choosing losses, and debugging training runs - Strong programming skills in C++/Python, plus a solid grounding in ML fundamentals: precision/recall trade-offs, class imbalance, evaluation metric selection, and rigorous experiment design We prefer: - Authorship of published papers in top-tier AI/ML, data mining, or computer vision conferences (e.g., NeurIPS, ICML, ICLR, KDD, CVPR, CoRL, SIGMOD, VLDB, ACL) - Research or applied experience with transformer and sequence models, multi-task learning, transfer learning or domain adaptation, and parameter-efficient fine-tuning of large pretrained models - Experience with JAX/Flax, distributed training on TPUs or GPUs, and large-scale data processing (MapReduce-style pipelines, SQL) for building training and evaluation datasets - Familiarity with autonomous driving, robotics, or simulation; and/or with probability calibration, uncertainty quantification, importance sampling, active learning, or rare-event and imbalanced-data modeling General Perks - Help solve challenging problems with a direct impact on the company - Competitive compensation packages with a housing/relocation bonus (if applicable) - Medical, dental, and vision insurance - Fun intern events and networking opportunities Onsite Perks - Free breakfast, lunch, dinner, and snacks - Free access to Google shuttles - Onsite gym Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly Masters Pay $70 - $70 USD The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85 - $85 USD
AI Engineer - Public Sector
Unstructured is defining the standard for enterprise data transformation in the age of LLMs and generative AI. In just two years, we've raised over $65M from world-class investors, including Menlo Ventures, Bain Capital, Databricks, NVIDIA, Microsoft, and IBM. Our open-source toolkit has been downloaded 97M+ times and is used by 90% of the Fortune 1000. We power production AI workflows across commercial and federal sectors — transforming PDFs, HTML, Word docs, images, emails, and more into AI-ready data pipelines that scale. We're not just building tools; we're building the backbone of generative AI and the infrastructure that unlocks intelligence across industries. THE MISSION At Unstructured, we are obsessed with transforming messy, unstructured data into a format that LLMs can actually use. Our Public Sector team has recently secured several high-impact contracts that demand more than just "off-the-shelf" solutions. We are looking for an AI Engineer who thrives at the intersection of R&D and production-grade software engineering. You won’t just be building notebook demos; you will be architecting, prototyping, and shipping novel multimodal data processing, RAG, and agentic systems that solve critical problems for Government and Military clients. Your work will bridge the gap between one-off custom builds and a repeatable, scalable product roadmap. WHAT YOU'LL OWN & DRIVE - You will be a high-agency individual contributor, owning the lifecycle of AI solutions from initial research to AWS deployment. - 50% Building & Shipping: Design and implement production-grade RAG pipelines and agentic workflows using Python. You’ll build systems that handle real-world "messy" data (PDFs, scanned docs, images, full motion video) and ensure they are performant and scalable. - 30% Research & Experimentation: Stay at the bleeding edge. You’ll evaluate new models (LLMs, embedding models, object detection), prototype approaches for SBIR/government deliverables, and run experiments to prove what actually works. - 20% Strategy & Collaboration: Partner with the team to document architectures, contribute to technical reports for contract deliverables, and participate in pre-sales calls to architect solutions for complex client needs. WHAT WE'RE LOOKING FOR We are looking for a self-directed engineer who excels in high-stakes, ambiguous environments. You are likely a strong fit if your professional background reflects the following: - Systems-First Engineering: You prioritize building reliable, scalable systems over experimental scripts. You have a track record of moving AI models out of notebooks and into production environments where latency, cost, and accuracy are treated as first-class citizens. - Technical Resourcefulness: You are comfortable working in restricted or air-gapped environments. When commercial APIs aren’t an option, you have the expertise to deploy, fine-tune, and optimize open-source models to achieve the mission objective. - Autonomous Problem Solving: You can take a high-level government requirement and translate it into a technical roadmap. You don't require constant oversight to identify the right tool for a job, whether it’s a specific vector database or a custom multimodal pipeline. - A "Generalist" Mindset: While you specialize in AI, you understand the full stack. You are as comfortable discussing embedding strategies as you are configuring AWS GovCloud infrastructure or debugging a FastAPI endpoint. MUST-HAVES - Proven experience deploying Production RAG pipelines against real-world, messy datasets. - Deep expertise in Agentic system design (tool-use, multi-agent orchestration). - Strong Python engineering skills—writing clean, scalable, and maintainable code - Experience operating within AWS/GovCloud environments. NICE-TO-HAVES - Experience fine-tuning NLP or object detection models. - Familiarity with LLM evaluation frameworks (hallucination detection, drift monitoring). - Knowledge of government security standards and working in different classification environments and on-prem - Security Clearance: Existing Secret/TS clearance or eligibility is a significant plus. YOUR TECHNICAL TOOLKIT - Languages: Python (expert-level), SQL - LLM & Agentic Frameworks: LangChain, LangGraph, CrewAI, or similar orchestration frameworks - RAG Stack: Retrieval with vector databases (Pinecone, Weaviate, Chroma, pgvector), graph databases (Neo4J), Elasticsearch, BM25, and Sentence-Transformers; NLP enrichment with spaCy, GLiNER, and Transformers; optimization using embedding models, reranking pipelines, and DSPy - Evaluation & Observability: RAGAS, DeepEval, Arize Phoenix, and synthetic annotations - Cloud & Infrastructure: AWS, SageMaker, Bedrock, S3, Lambda, Docker, and FastAPI - Data Processing: Complex pipelines for unstructured and multimodal data, including PDFs, scanned documents, images, and audio. WHY YOU'LL LOVE IT HERE You'll be surrounded by smart, kind, low-ego people who genuinely enjoy building together. We invest in our team with company offsites, best-in-tech swag, and the tools you need to do your best work, wherever you're based. We support you holistically, not just at work. From medical, dental, and vision coverage effective the 1st of the month following your start date, life and disability insurance, unlimited PTO, and flexible parental leave, to a 401(k) with company match, equity, a $500 work from home stipend, $70/month internet reimbursement, and team/company offsites throughout the year - we want you focused on building, growing, and staying energized for the long haul. If you're excited about what we're building, we'd love to meet you. Unstructured is proud to be an Equal Opportunity Employer. We're committed to building a diverse team and an inclusive culture where everyone can do their best work. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
ML & Agentic Systems Engineer [IC4]
Who we are Our mission is to bring clarity and control to the world's most complex codebases. AI is accelerating code creation, but the infrastructure to understand, oversee, and evolve that code hasn't kept pace. Sourcegraph gives engineering organizations full visibility across their systems, precise context for their agents, and the ability to execute coordinated code changes at scale. As agentic development becomes the dominant engineering paradigm, we provide the context layer teams need to take control of their codebase. With Code Search, Deep Search, MCP, and Agentic Batch Changes, we deliver on that mission today - giving engineering teams and their AI tools the cross-repo context to navigate massive codebases with confidence, and the ability to make changes across hundreds of repositories at once. Companies like Stripe, Reddit, and Leidos rely on Sourcegraph to ship faster and with higher quality. We're backed by a16z, Sequoia, and Redpoint, and proud to operate as a globally distributed team that values high agency, direct communication, and customer love. If you want to build the infrastructure that lets every engineering team - and every agent they deploy - operate on their codebase with confidence, join us. Hours & location 🌎 While we hire almost anywhere in the world, we have a preference for someone to reside in the following locations for this role. However, if you feel qualified, we welcome you to apply regardless of location. No matter what, working hours must overlap with EST for at least 20 hours/week. Preferred locations: - Europe - North America Why this job is exciting Sourcegraph is at the forefront of building AI tools to solve the biggest problems in the software industry, problems that only get bigger as codebases grow and as more of the work is done by agents. The Code Understanding team owns the surfaces where that intelligence meets the developer: Deep Search , our agentic, multi-step answer engine across an enterprise's entire lineup of codebases, Query Assist , turning natural language queries into Sourcegraph query syntax, Smart Hovers , concisely summarizing symbols right where devs need it, guided diff review, and the APIs that both humans and AI agents rely on every day. Most of what makes those surfaces tick is agent engineering : a blend of software engineering, machine learning, and statistics. Agent engineering tells us which model to use and when, how to retrieve and pack context, how to measure answer quality, where to fine-tune or distill a smaller model to cut costs and latency, and how to expand a single LLM call into a reliable multi-step agent. As the staff ML and agent engineer on Code Understanding , you'll be the technical owner of it: setting the tam’s direction for models, evaluations, and agentic systems, making our products measurably better, faster, and cheaper, and raising the team's fluency in building with models. This is a staff-level role : we're hiring a technical leader, not just a strong individual contributor. The primary need is a production ML and evaluation authority who also builds production agent systems. You'll own the hardest, most ambiguous problems in this space, set standards others follow, and influence direction beyond your immediate team. You'll get the exhilarating chance to drive the vision on how we can provide the best code understanding experience on the market by combining our deterministic, large-scale systems and AI into experiences never seen before. Concretely, you'll own work like: - Agentic systems. You'll design and harden the multi-step, tool-using agent loops behind current and new agentic experiences, turning research and experiments into reliable, observable, and affordable products at enterprise scale. - Pragmatic use of evaluations. Crafting agentic products means needing to tell when a change actually helped, which is hard when agents keep changing and the product keeps shifting. You'll bring judgment about where evaluations earn their keep, when to use targeted smoke tests and metrics, and how to avoid noise dressed up as rigor, so we can move fast with confidence. - Models: selection, upgrading, and training. You'll decide which models we run where, drive upgrades, and fine-tune our own when that's the right call. - Retrieval and context engineering. You'll push on how we ground models in a customer's code - retrieval, ranking, context windows, citations - to make answers more accurate and verifiable. - Cost and latency. Every surface has a per-user economic budget. You'll treat cost and latency as product features, and profile, distill, cache, and right-size models so we can ship ambitious features sustainably. You'll do this on a small, senior-leaning team that ships quickly, owns a lot of product surface, and has streamlined product management: engineers here talk to customers, frame the problem, and own it end-to-end. You'll have real agency over technical direction and a direct line to the impact of your work. 📅 Within one month, you will… - Get the Code Understanding products and their model/agent pipelines running end-to-end locally, and land your first improvements to a model, prompt, retrieval path, or eval. - Build a clear picture of where the AI engineering pain is, and the product surfaces most constrained by them. - Get to know the team and our customers, and start forming your own opinions about where our agentic products should go next. - Join the team's on-call support rotation. 📅 Within three months, you will… - Own a meaningful agentic slice of the product end-to-end, driving it from problem-framing through rollout and measurement. - Establish how the team ships model and prompt changes responsibly: the evals, dashboards, and guardrails that make quality and cost regressions visible before customers feel them. - Begin up-leveling teammates in building with models by pairing with them, reviewing their code, and modeling good agent engineering instincts. 📅 Within six months, you will… - Be the recognized technical authority for agent engineering and agentic systems on Code Understanding. You will be the person teammates, and increasingly the wider department, defer to on model, eval, and agent-design decisions. - Have measurably moved the products: better answer quality, lower cost/latency, or new agentic capabilities that weren't feasible before. - Be setting the direction of the team's roadmap where it intersects agents, bringing conviction, backed by evidence, about which bets are worth making, and pulling other engineers up to execute on them. About you You are a staff engineer and technical leader with hard-won skills across production machine learning, evaluation, and agent systems . This high-leverage role relies on your ability to make sound model and evaluation decisions for a fast-moving product, build the production systems around them, steer technical direction, and be a force multiplier for a talented, product-minded team. You're equally comfortable reasoning about an eval harness, a fine-tuning run, a retrieval pipeline, and the multi-step agent loop that ties them together, and you make everyone around you better at all of it. You operate at staff scope: you own the most ambiguous, highest-risk problems in your domain, go into whatever codebase a problem requires, set standards and patterns others adopt, and translate fluidly between engineering goals and business objectives. You influence direction beyond your immediate team. You lead through technical excellence and mentorship. - You have personally owned a production model lifecycle. You have trained or fine-tuned at least one model and taken it from dataset construction through evaluation, production rollout, and monitoring. You can explain how you chose between training or fine-tuning and prompting or retrieval, how you chose baselines and metrics, what error analysis revealed, and how production evidence affected the next version. - You build agents, fluently and opinionatedly. You've designed multi-step agentic systems and made them reliable, observable, and cost-bounded. You have a point of view on where agents shine and where deterministic code or human judgment is required. - You have strong evaluation judgment. You build representative datasets, meaningful baselines, useful error taxonomies, and release criteria that connect offline measurements to production behavior. You know where rigorous evaluations earn their keep, where lightweight smoke tests or qualitative review are enough, and where a precise-looking metric is misleading. - You treat cost and latency as product constraints. You make measured quality, latency, and cost tradeoffs and use the appropriate combination of model selection, prompting, retrieval, caching, distillation, and fine-tuning rather than reaching reflexively for a more complex model. - You operate autonomously on ambiguous problems. Given a rough product idea, a few customer quotes, and a Slack thread, you come back with a plan, a prototype, milestones, and a point of view on tradeoffs, without it being pre-scoped. You own high-technical-risk projects end-to-end. - You contribute beyond your domain. As a senior IC, you go into whatever part of the codebase a problem requires, recognize issues beyond your immediate area, and translate between engineering goals and business objectives. - You up-level the people around you. You mentor by pairing on hard problems, providing substantive design and code reviews, and spreading agent engineering literacy across the team. You see investing in your teammates' growth as part of the job. - You're customer and product-driven. You're comfortable on customer calls and in feedback threads, you turn raw signals into requirements, scopes, and milestones, and you push back when feedback would lead the product astray. - You're pragmatic, not a perfectionist. You ship the smallest correct thing, prefer robust solutions over complicated ones, and keep a high-quality bar with simplicity. On the engineering fundamentals: - You're a strong software engineer who can ship production services. - You're comfortable across our stack - Go on the backend, TypeScript on the frontend, GraphQL, Postgres, Docker - or you're clearly able and eager to get there. - You're fluent with agentic coding tools, and you understand and own every line it submits. - You're comfortable in an async-first, multi-service, fast-paced remote environment. Nice-to-haves: - You've shipped an LLM-powered or agentic developer-facing product you can speak about opinionatedly: what worked, what didn't, what you'd do differently. - You've fine-tuned, distilled, or trained models to meet cost, latency, or quality targets in production. - Experience with retrieval, ranking, embeddings, or search relevance. - Experience working directly with enterprise customers and translating their needs into a product. - Experience mentoring or up-leveling engineers, especially raising a team's agent engineering fluency. Level 📊 This job is an IC4. You can read more about our job leveling philosophy in our Handbook. Compensation 💸 We pay above-market salaries because we want to hire exceptional people who can focus on building great products, not worrying about paying bills. As an open and transparent company, our compensation philosophy and pay bands are visible to every Sourcegraph teammate, and we strive to make our approach equitable, explainable, and competitive. Your base salary is determined by the IC4 pay band for your location zone (1-4). Our pay bands are informed by market data and designed to ensure competitive compensation wherever you live. During the recruiting process, we'll discuss the range applicable to you based on job level, relevant skills, experience, qualifications, and location zone. 💰 The starting salary for the IC4 pay band in each zone is: - Zone 2: $176,000 USD - Zone 3: $132,000 USD - Zone 4: $88,000 USD 📈 In addition to competitive cash compensation, we offer meaningful equity (because when Sourcegraph succeeds, we want you to succeed, too) and generous perks & benefits . Interview process Below is the interview process you can expect for this role (you can read more about the types of interviews in our Handbook). It may look like a lot of steps, but rest assured that we move quickly and the steps are designed to help you get the information needed to determine if we’re the right fit for you… Interviewing is a two-way street, after all! We expect the interview process to take 4.75 hours in total. 👋 Introduction Stage - we have initial conversations to get to know you better… - [30m] Recruiter Screen - [45m] Hiring Manager Screen / Resume Deep Dive 🧑💻 Team Interview Stage - we then delve into your experience in more depth and introduce you to members of the team, including cross-functional partners… - [60m] Technical Interview - [60m] Technical Interview - [60m] Cross-functional team collaboration / Values 🎉 Final Interview Stage - we move you to our final round, where you gain a better understanding of our business and values holistically… - [30] Leadership - We check references and conduct your background check Please note - you are welcome to request additional conversations with anyone you would like to meet, but didn’t get to meet during the interview process. Learn more about us You can learn more about what it is like to work at Sourcegraph by reading our handbook . We are an ambitious team who are collectively working hard to build the most influential company in the world. You can read more about our culture , competitive compensation and benefits here. Sourcegraph is an equal opportunity workplace; we welcome people from all backgrounds. Sourcegraph participates in E-Verify for U.S. Employees.
Senior Principal AI Solutions Engineer
SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform for customers worldwide. At the core of SambaNova's technology is the RDU (Reconfigurable Dataflow Unit) — a chip built on a dataflow architecture rather than the traditional GPU model. Its decode performance is especially strong for agentic workloads like multi-turn agents, code generation, and long-running applications. RDUs are packaged into SambaRack, rack-scale hardware that lets customers deploy state-of-the-art models with better performance, greater energy efficiency, and faster time to value. We are looking for a Senior Principal AI Solutions Engineer: a hands-on technical leader who sets the technical direction for our solutions portfolio and still builds. This is someone who can go from a customer problem to a polished, working, agentic application in days, and then harden it for production. You will work across the whole modern AI stack: open-weight models, agent frameworks and tool use, self-improving evaluation loops, multimodal and voice pipelines, and the front end that makes it all usable by knowledge workers. You will lead hands-on engagements with our most strategic customers, mentor and raise the bar for the wider solutions team, and feed what you learn back into the platform. Responsibilities Build agentic and self-improving systems - Agentic AI Architecture: Design and build single- and multi-agent systems with planning, tool and function calling, MCP, memory, and long-running workflows that are reliable enough for production • Self-Improving Systems: Build feedback loops that make agents better over time, including automated evals, LLM-as-judge, trace-driven prompt and program optimization (e.g. DSPy-style), synthetic data generation, and fine-tuning from production signals - Evaluation and Observability: Set up eval harnesses, tracing and guardrails so customers can measure quality, cost and latency, and trust what they deploy Get the most from open models - Open-Weight Model Expertise: Choose, adapt and combine open models (e.g. Llama, Qwen, DeepSeek, gpt-oss, Gemma, GLM) for customer use cases, including LoRA/PEFT fine-tuning, distillation and model routing - Performance and Benchmarking: Benchmark end-to-end solutions, not just tokens per second, and show where fast inference changes what an application can do - Model Integration: Validate new models and capabilities on SambaNova's platform stack and report gaps to Product and Engineering Ship full-stack vertical solutions - Front-End and Product Build: Build polished, usable web applications (e.g. React/Next.js, TypeScript) that non-technical knowledge workers can adopt, not just notebooks and APIs - Vertical Knowledge Work: Build domain solutions for areas such as financial services, legal, healthcare, public sector and research: document analysis, research agents, RAG and knowledge assistants, report generation and workflow automation - Multimodal Solutions: Build vision-language and document-understanding pipelines (OCR, charts, forms, images, video) combined with agentic reasoning - Audio and Voice: Build real-time voice agents and audio pipelines (ASR, TTS, speech-to-speech, streaming and turn-taking) where low latency is the product Lead with customers and shape the platform - Solutions Technical Strategy: Set the technical direction for the solutions portfolio: which agentic, multimodal and vertical patterns we invest in, the shared frameworks and components we build, and the engineering standards they meet - Technical Leadership and Mentorship: Act as the senior technical authority across solutions engineering; review designs, mentor engineers and lift the quality of everything the team ships - Executive Engagement: Act as the trusted technical advisor to customer CTOs and AI leaders on architecture, build-versus-buy and scaling AI across the enterprise - Customer Engagement: Lead technical discovery, workshops, hackathons and hands-on co-builds with strategic customers, from prototype to production - Reference Architectures: Publish reference architectures, starter kits, open-source examples and best-practice guides that scale beyond each engagement - SambaStack Tooling: Develop tooling and automation for SambaStack deployment, management and integration - Product Feedback: Feed what you learn in the field (model requests, feature gaps, developer experience) into the model roadmap and platform priorities - Thought Leadership: Represent SambaNova through demos, talks, blogs and community contributions Requirements Required Qualifications - Bachelor's degree or higher in Computer Science, Electrical Engineering, Applied Mathematics, Physics, Statistics or a related field - 8+ years (IC5) or 10+ years (IC6) of industry experience in software, ML or solutions engineering, including 3+ years building LLM-based applications that reached production - Proven experience building agentic systems: tool and function calling, multi-agent orchestration and frameworks such as LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK or equivalent • Hands-on experience with open-weight models: serving, prompting, fine-tuning (LoRA/PEFT) and evaluation • Strong full-stack skills: expert Python plus modern front-end development (TypeScript, React/Next.js), APIs and cloud deployment - Experience designing evals and benchmarks for LLM applications, covering quality, latency and cost • Excellent customer communication, with the ability to lead workshops and explain architecture to both executives and engineers - A track record of technical leadership across teams: setting architecture direction, mentoring senior engineers and owning outcomes for strategic accounts Preferred Qualifications - Experience building self-improving or learning systems: prompt/program optimization, RL from feedback, synthetic data pipelines or continual fine-tuning - Experience building real-time voice agents (e.g. LiveKit, Pipecat, WebRTC) and working with speech models (ASR/TTS) - Experience with multimodal and vision-language models and document-understanding pipelines • Domain experience in one or more knowledge-work verticals (financial services, legal, healthcare, public sector, scientific research) - Familiarity with inference frameworks (vLLM, SGLang, TensorRT-LLM) and hardware-aware performance tuning - Experience with MCP, RAG at enterprise scale, and agent security and guardrails • Open-source contributions, public demos or technical content in the AI community Base Salary Range: Base Pay Range $234,000 - $286,000 USD Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified. EEO Policy SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws. Benefits Summary for US-Based, Full-Time Employment Positions SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.
Head of GenAI Platform
ABOUT US Founded in 2019 and part of Y Combinator's 2020 cohort, Photoroom https://www.photoroom.com is the leading visual solution for e-commerce. We've raised Series B funding and reached 300+ million users worldwide, processing over 5 billion images annually and serving both small businesses and major enterprises like Amazon, DoorDash, and Decathlon through our mobile app, web platform, and API. We're a team of 100+ passionate builders giving e-commerce businesses superpowers to create visuals that help them grow, making the hardest parts of selling online disappear. We focus on craft, innovation, and collaboration to create exceptional impact for e-commerce businesses worldwide. HEAD OF GENAI PLATFORM ROLE SUMMARY We're looking for a Head of GenAI Platform to lead the strategy and technical direction behind Photoroom's generative AI platform, spanning model orchestration, evaluation, agentic workflows, and foundation model training, enabling our teams to ship breakthrough AI products faster than anyone else. You'll partner closely with our Infrastructure team on inference infrastructure and model serving, rather than owning that layer outright. This is a highly strategic leadership role combining strong technical grounding, organisational leadership, and product thinking. Deep technical expertise already exists across our ML and Research teams, so we're not looking for the single deepest expert in the room, we're looking for someone who has been hands-on in ML or research themselves, understands the space well enough to lead a credible technical conversation and make sound calls, and can create the conditions for that expertise to thrive. You'll work closely with our CTO and senior leaders across Product and Engineering to shape how AI is built, deployed, and scaled across the company. Compensation: Competitive salary + meaningful equity Location: Paris, France (Hybrid) Relocation: Visa sponsorship and relocation support up to €10,000 available ABOUT THE ROLE You'll be responsible for building both the technical foundations and the organisation that enable Photoroom's next generation of AI products. Your responsibilities will include: - Defining the long-term vision and technical strategy for our GenAI Platform around model orchestration, evaluation, agentic workflows and foundation model training - Partnering with our Infrastructure team on inference infrastructure, model serving and reliability, so the platform stays scalable and cost-efficient across billions of inference requests - Partnering with Research to rapidly bring new models into production - Leading and growing a multidisciplinary team of ML Engineers, Software Engineers and Researchers, setting direction for the team as a whole, while day-to-day management of individual contributors continues through the team's existing engineering leads - Creating an environment where ambitious technical ideas can move from research to production in days rather than months - Building evaluation frameworks that measure model quality, cost and latency consistently across the platform - Working closely with Product, Engineering and Executive leadership to ensure platform investments directly accelerate product innovation WHAT YOU'LL BRING We're looking for someone with strong technical grounding and proven leadership experience. You don't need to be the foremost technical authority in every area, as that depth already exists in the team, but you should have been a hands-on ML/research practitioner at some point in your career, ideally with some research experience, so you can lead the function credibly. You'll likely have: - Significant experience leading Machine Learning or AI organisations within product-focused companies, having started your career as a hands-on ML engineer or researcher before moving into leadership - Experience building and operating AI platforms: model orchestration, evaluation pipelines or agent frameworks, with enough grounding in inference infrastructure and model serving to partner effectively with an Infrastructure team, without needing to own that layer directly - Strong technical expertise across Generative AI, Computer Vision or Foundation Models - A track record of taking emerging technologies from research into production systems used by millions of users. We don't expect deep, specific agentic-workflow experience, the field is relatively new and nobody has a long track record there, but we do want evidence you can effectively leverage new technologies to productise what's next - Experience growing high-performing engineering and research teams through hiring, coaching and organisational design - Enough technical depth to follow and meaningfully contribute to an architecture conversation outside your core area, you don't need all the answers, but you must understand the building blocks well enough to ask the right questions and make a call when needed - Strong product instincts and the ability to balance long-term technical investments against immediate customer impact - Excellent communication skills and experience partnering with Product, Engineering, Marketing and Executive stakeholders WHAT SUCCESS LOOKS LIKE Within your first 12-18 months, you'll have: - Established a clear long-term strategy for Photoroom's AI platform - Built infrastructure that significantly reduces the time required to ship new AI capabilities - Enabled research teams to move new models into production rapidly and reliably - Improved platform performance, reliability and infrastructure efficiency at scale, in close partnership with the Infrastructure team - Built and retained a world-class team of ML Engineers, Researchers and Platform Engineers - Created evaluation frameworks that allow model quality, cost and latency to be measured consistently across the platform - Helped define the technical direction that underpins the next generation of AI-powered products at Photoroom WHY THIS ROLE MATTERS Every AI capability at Photoroom depends on the platform your team builds. From foundation model training to model orchestration, evaluation pipelines and production inference, you'll shape the technical backbone that enables our teams to innovate quickly while operating at massive scale. This is an opportunity to influence both the long-term direction of our AI strategy and the systems that process billions of images every year. ABOUT PHOTOROOM Photoroom was launched in 2020 after being accepted into Y Combinator and has grown into one of the world's leading AI photo editing platforms. Today we have: - Over 300 million downloads - More than 5 billion images processed annually - Millions of active users across mobile, web and API - Customers ranging from individual creators to global brands including Amazon, Shopify, eBay, DoorDash, Decathlon and Depop We're profitable, growing rapidly and continuing to invest heavily in AI research and product innovation. Our teams train our own foundation models, operate large-scale GPU infrastructure, and build AI products used every day by millions of people worldwide. WORKING AT PHOTOROOM We believe great people do their best work when given ownership, autonomy and ambitious problems to solve. We offer: - Competitive salary and meaningful equity - Hybrid working from our Paris headquarters - Visa sponsorship and relocation support - MacBook Pro and full home office setup - 30 days annual leave - Private healthcare - Regular company off-sites and team retreats - A collaborative, high-performance engineering culture focused on shipping impactful AI products HIRING PROCESS - Recruiter conversation - Leadership & values conversation with our CTO - Technical screening - Technical case study (working session) - Leadership and cross-functional interviews - References - Offer DIVERSITY, EQUITY, INCLUSION, AND BELONGING We're committed to enabling everyone to feel included and valued at work. We believe our company and culture are strongest when composed of diverse experiences and backgrounds. That's also why we have flexible working hours, trust people to work in a hybrid environment, and extended parental leave. All qualified applicants receive consideration for employment without regard to age, color, family, gender identity, marital status, national origin, physical or mental disability, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws.
Solution Architect - AI Labs
NVIDIA are seeking Solution Architects with specialized expertise in training and deploying Large Language Models (LLMs), implementing RAG workflows, and agentic inference. You will leverage the full NVIDIA software & hardware ecosystem to design, optimize, and deliver production-grade generative AI solutions for enterprise customers. With competitive salaries and a generous benefits package, we are widely considered to be one of the world’s most desirable employers! We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous person with a real passion for technology, we want to hear from you. What You’ll Be Doing: Conduct in-depth analysis of customers' latest needs and co-develop accelerated computing solutions with key customers. Assist in supporting industry accounts and driving research/influencing/new business in those accounts. Deliver technical projects, demos and client support tasks as directed by the Solution Architecture leadership team. Understand and analyze Top AI Labs customers' workloads and demands for accelerated computing, including but not limited to: LLM training/inference acceleration and optimization, application optimization for Agent AI/RAG, kernel analysis, etc. Assist Top AI Laps customers in onboarding NVIDIA's software and hardware products and solutions, including but not limited to: CUDA, TensorRT-LLM, NeMo Framework, etc. Be an industry thought leader on integrating NVIDIA technology into applications built on Deep Learning, High Performance Data Analytics, Robotics, Signal Processing and other key applications. Be an internal champion for Data Analytics, Machine Learning, and Cyber among the NVIDIA technical community. What We Need To See: 3+ years’ experience with research/development/application of Machine Learning, data analytics, or computer vision work flows. Outstanding verbal and written communication skills. Ability to work independently with minimal day-to-day direction Knowledge of industry application hotspots and trends in AI and large models. Familiarity with large model-related technology stacks and common inference/training optimization methods.C/C++/Python programming experience Desire to be involved in multiple diverse and innovative projects Experience using scale-out cloud and/or HPC architectures for parallel programming MS or PhD in Engineering, Mathematics, Physics, Computer Science, Data Science, Neuroscience, Experimental Psychology or equivalent experience. Ways To Stand Out From The Crowd: AIGC/LLM/NLP experience CUDA optimization experience. Experience with Deep Learning frameworks and tools. Engineering experience in areas such as model acceleration and kernel optimization. Extensive experience designing and deploying large scale HPC and enterprise computing systems.
Senior Deep Learning Engineer, Cosmo 3D Spatial
NVIDIA is at the heart of the AI revolution, and Physical AI is its next frontier: machines that perceive, reason about, and act in the three-dimensional world. NVIDIA Cosmos is our open platform of world foundation models for Physical AI, built to interpret images, video, and text and turn them into a structured understanding of a physical scene: motion, object interactions, geometry, and physical context. These models are the reasoning layer for robots, autonomous vehicles, and smart infrastructure. The Cosmos Engineering team builds the foundational capabilities behind these models. We are hiring a Senior Deep Learning Engineer to own the data engine and end-to-end training verification behind the 3D spatial reasoning and perception capabilities of these models : 2D and 3D grounding, metric geometry, spatial reference frames, cross-view correspondence, and embodied spatial reasoning. You will decide what the model learns geometry from, prove that it learned it, and work directly with research scientists in Cosmos Lab to turn 3D research hypotheses into measurable capability in shipped models. If you believe frontier model quality is won or lost in the data and the evaluations, this is the seat where that belief does the most work. What you'll be doing: Own the 3D data engine for Cosmos spatial reasoning: source, curate, filter, and balance large-scale real-world image and video corpora into vision-language training data with the coverage and diversity that spatial understanding demands. Build the annotation and auto-labeling pipelines that produce 3D-grounded supervision at scale, camera-relative 3D boxes, referring and spatial question answering, free space and reachability, ego-, world-, and object-centric reference frames, cross-view correspondence, camera motion, distance and size, and chain-of-thought traces, validated by programmatic and model-based critics. Own data quality end to end: semantic deduplication, automated quality scoring for faithfulness, completeness, and correctness, coverage analysis across scene types and reference frames, and the sampling strategies that keep pre-training and supervised fine-tuning mixtures balanced. Verify end-to-end model training: run and validate full pre-training and supervised fine-tuning pipelines, guard reproducibility, catch data and checkpoint regressions, diagnose throughput and loss anomalies, and attribute capability changes back to the specific data and recipe decisions that caused them. Build and operate the 3D and spatial evaluation suite, public benchmarks such as CV-Bench, BLINK, RefSpatial, VSI-Bench, SPAR-Bench, and RoboSpatial, NVIDIA's VANTAGE-Bench for real-world fixed-camera video understanding, and in-house benchmarks you design with continuous evaluation and full traceability from every reported score back to the exact weights, inputs, configuration, and evaluation code. Partner closely with Cosmos Lab research scientists: translate 3D research hypotheses into dataset and ablation experiments, run them at scale, and feed honest results back into recipe and architecture decisions. Operate on large multi-node GPU clusters, tuning data throughput, sharding, and dataloader performance so that data is never the bottleneck on a long training run. Ship the results into Cosmos releases, open-source datasets and benchmarks where appropriate, and raise the bar for data and evaluation rigor across the team. What we need to see: MS or PhD in Computer Science, Electrical/Computer Engineering, Robotics, or a related field, or equivalent experience. 12+ years of proven experience building deep learning systems in Python with PyTorch or JAX on Linux. Deep expertise in 3D computer vision, multi-view geometry, structure-from-motion or SLAM, depth and camera pose estimation, point cloud processing, or 3D reconstruction with the practical ability to produce and validate 3D ground truth at scale, not just consume it. Hands-on experience with vision-language models, including building the training data and evaluations that measurably improve visual grounding and reasoning quality. Demonstrated experience building large-scale multimodal data pipelines: distributed video and image processing, deduplication, captioning and annotation, automated quality metrics, and dataset versioning. Experience running and validating large model training on multi-GPU, multi-node clusters, with working knowledge of distributed training and sharding strategies such as data, tensor, and pipeline parallelism or FSDP. Rigorous evaluation methodology: designing benchmarks that resist gaming, building clean ablations, and reading results honestly enough to kill your own ideas. Excellent written and verbal communication, with a track record of partnering effectively with research scientists and translating research direction into engineering execution. Ways to stand out from the crowd: PhD and/or publications at CVPR, ICCV, ECCV, NeurIPS, ICLR, or CoRL in 3D vision, multimodal learning, or embodied AI. Experience curating web-scale or petabyte-scale video corpora, and building the distributed processing infrastructure behind it using Ray, Spark, Slurm, or similar. Familiarity with 3D foundation models and modern auto-labeling techniques for geometry, camera estimation, and correspondence. Experience with vision-language model post-training, supervised fine-tuning, chain-of-thought data design, reward modeling, or reinforcement learning with verifiable rewards applied to reasoning quality. Experience building evaluation infrastructure and harnesses such as VLMEvalKit, including leaderboards, dashboards, and example-level failure inspection. With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and versatile people in the world working with us, and our engineering teams are growing fast in some of the most impactful fields of our generation: Deep Learning, Artificial Intelligence, and Physical AI. If you're a creative engineer who enjoys autonomy and shares our passion for technology, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 19, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Robotics Research Engineer - Robot Simulation and...
In the NVIDIA Isaac team, we build tools that bring the power of the GPU to bear on one of today’s grand challenges: physical AI. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in an inclusive, encouraging environment where we all work together to advance the frontiers of accelerated computing. We’re looking for a research engineer to join our team to develop simulation tools built for scientists and companies that are using machine learning to solve the future of robotics. In this role, you will drive cross-team collaboration between Isaac product engineering and NVIDIA Robotics research: translating research ideas into product-ready Isaac Lab Arena capabilities, bringing product and user feedback back into the research loop, and driving adoption with research users. You'll join a team of robotics engineers who work at the intersection of simulation and machine learning. Our goal is to build the industry's leading tool for evaluating robot foundation models in simulation. This mission builds on NVIDIA's history of creating virtual worlds using its groundbreaking technology on photorealistic rendering and physics simulation technologies. What you'll be doing: Building simulation frameworks for training and evaluating robot foundation models on top of NVIDIA's Omniverse platform. Partner directly with research to design, implement, and evaluate novel algorithms for robot learning. Driving technical architecture, setting evaluation standards, and cross-team leadership between Research and Product. Working as part of a high-paced software engineering team: design/code reviews, testing, continuous integration, deployment. Training and evaluating robot foundation models. Integrating modern LLM and agentic workflows into simulation and robotics workflows. Working with researchers and product engineers to translate research ideas into Isaac Lab Arena capabilities that can scale to NVIDIA's user base. Keeping up to date with the state-of-the-art in robotics research. What we need to see: PhD degree in Computer Science, Robotics, or a related field (or equivalent experience). 8+ years of relevant industry or post-degree research engineering experience in robotics, simulation, or robot learning, with ability to translate research prototypes into product or platform capabilities. Proven track record of software design and translating research prototypes into production-ready software platforms. Proficiency using Python and development experience with deep learning software stacks (Pytorch, Jax, etc.). Ways to stand out from the crowd: High-impact publications at top robotics and machine learning venues (e.g., CoRL, RSS, ICRA, NeurIPS). Robot learning expertise with reinforcement learning and/or imitation learning, Deep expertise in GPU-accelerated physics simulation engines (e.g., PhysX, Isaac Gym/Isaac Lab, Newton). Experience with NVIDIA robotics ecosystem tools (CUDA, warp, Isaac Lab, ROS). Developing and maintaining open-source projects. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 22, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
AI Computing Software Development Intern, LLM Infe...
We are now looking for a Software Development Engineer to help TensorRT LLM and TensorRT Edge LLM projects! NVIDIA is hiring software engineers for its AI Computing team. Academic and commercial groups around the world are using GPUs to power a revolution in deep learning-powered AI, enabling breakthroughs in areas like LLM, ChatGPT and GenerativeAI that has put DL at the “iPhone moment” for AI. Join the team which is building the inferencing software which will be used across our product lines! The ability to work on a fast-paced delivery-focused team is required and excellent interpersonal skills are a must. What you'll be doing: Craft and develop robust inferencing software that can be scaled to multiple platforms for functionality and performance Performance analysis, optimization and tuning Closely follow academic developments in the field of artificial intelligence and large language models Provide feedback into the architecture and hardware design and development Collaborate across the company to guide the direction of machine learning inferencing, working with software, research and product teams Publish key results in scientific conferences What we need to see: Pursuing Masters or higher degree in Computer Engineering, Computer Science, Applied Mathematics or related computing focused degree Relevant software development experience. Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design. Strong curiosity about artificial intelligence, awareness of the latest developments in deep learning like LLMs, generative and recommender models Experience working with deep learning frameworks like TensorFlow and PyTorch Proactive and able to work without supervision Excellent written and oral communication skills in English NVIDIA is widely considered to be one of technology’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. Does the idea of contributing to and pushing the boundaries of state-of-the-art AI and Compute systems excite you? Interested in getting exposure to the entire DL SW stack? Come join us and help build the GPU-accelerated DL platform used worldwide.
Senior / Engineer II, AI Lab Research Engineer
Your Impact at LILA Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai At Lila, we are uniquely cross-functional and collaborative. We are actively reimagining the way teams work together and communicate. Therefore, we seek individuals with an inclusive mindset and a diversity of thought. Our teams thrive in unstructured and creative environments. All voices are heard because we know that experience comes in many forms, skills are transferable, and passion goes a long way. If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, please apply. What You'll Be Building - Agentic AI for science: Systems that perform sequential decision‑making and multi‑step reasoning to solve domain‑specific problems. - Workflow/code generation: From natural language intent to typed, executable steps for lab instruments. - Evaluation & reliability: Benchmarks, test suites, and telemetry to measure capability and quantify progress toward scientific goals. What You’ll Need to Succeed - PhD or Masters in a quantitative discipline (e.g., Computer Science, Physics, Mathematics, Engineering) with a strong background in machine learning and one domain of science (e.g. biology or materials science). - Strong grasp of LLMs and agent architectures (planning, tool use, structured function calling, code generation) and how to adapt them to domains. - Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experience implementing scalable solutions for complex tasks. - Comfort collaborating across disciplines and interfacing with simulations and real lab systems. Bonus Points For - Building long‑horizon agents or RL for control/decision‑making; experience with model‑based or offline RL. - Designing domain‑specific benchmarks and evaluation harnesses for complex scientific tasks. - Digital‑twin development, calibration, and sim‑to‑real transfer. - Publications or open‑source contributions in AI for science (especially publications in top-tier conferences like NeurIPS, ICML, AAAI, ICLR). Location San Francisco, CA or Cambridge, MA (Hybrid and On-Site available depending on team needs). Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $148,000 - $240,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Scientist II / Senior ML Scientist, Data-Efficient...
Your Impact at LILA Lila Sciences is seeking a Machine Learning Scientist, Data-Efficient Learning for Drug Discovery to build models and learning strategies for settings where data is scarce, expensive, and intentionally generated. This role is focused on training useful models from low-quantity but high-quality datasets ranging from as few as tens to low thousands of examples, often in tightly focused areas of chemical space, and deciding what data should be acquired next. This is an applied scientific ML role in a frontier research area. The work is not a matter of applying standard models out of the box. You will use and develop approaches across active learning, meta-learning, fine-tuning, uncertainty estimation, experimental design, and multimodal modeling to help Lila build closed-loop systems that learn efficiently from targeted data acquisition. This role connects model training with scientific decision-making: data acquisition plans should be useful to computational chemists evaluating compound priorities, computational biophysicists deciding when simulation is warranted, and cofolding modelers deciding which protein-ligand data would improve structure-aware models. What You'll Be Building - Build ML models that perform well in low-data regimes for drug discovery and molecular optimization. - Design data acquisition strategies that identify which compounds, assays, DEL selections, simulations, structural predictions, or experiments should be run next to maximize learning. - Develop active learning, meta-learning, fine-tuning, transfer learning, and uncertainty-aware modeling approaches for focused chemical spaces. - Train models on low-quantity, high-quality datasets generated by Lila's experimental, computational, and agentic discovery systems. - Build multimodal models that can integrate DEL data, simulation outputs, assay data, protein and structural information, chemical features, literature or text-derived signals, images, and experimental metadata. - Partner with experimental, computational, and drug discovery teams to ensure data acquisition plans are scientifically meaningful and operationally feasible. - Evaluate models through learning curves, prospective validation, retrospective benchmarks, uncertainty calibration, and decision-focused metrics. - Develop closed-loop learning workflows that continuously update models as new data arrives from experiments, simulations, and automated systems. - Translate model predictions and uncertainty into practical recommendations for compound selection, assay selection, batch design, or next experiments. - Work with platform and agent teams to expose model-driven recommendations as tools for scientists and AI agents. What You'll Need to Succeed - PhD or equivalent experience in machine learning, computational chemistry, computational biology, statistics, computer science, bioengineering, or a related field. - Strong experience training ML models in low-data regimes. - Experience with active learning, Bayesian optimization, experimental design, meta-learning, fine-tuning, transfer learning, uncertainty estimation, or related data-efficient learning methods. - Experience building ML models for scientific, molecular, biological, chemical, pharmacological, biochemical, or other high-dimensional experimental datasets. - Experience with multimodal learning or methods that combine heterogeneous data sources. - Ability to reason about data acquisition strategy, not only model fitting. - Strong scientific judgment and ability to connect model behavior to experimental decisions. - Practical experience with PyTorch, JAX, scikit-learn, or equivalent ML tools. - Ability to collaborate across ML, data, computational science, experimental, and drug discovery teams. Bonus Points For - Drug discovery experience, especially in molecular optimization, screening, or design-make-test-learn workflows. - General understanding of pharmacology, biochemistry, or mechanisms of molecular activity. - Experience with DEL, high-throughput screening, medicinal chemistry, assay data, simulation-derived features, protein or structure-based features, text or literature features, or scientific images. - Experience with closed-loop experimentation, autonomous labs, or agent-driven scientific workflows. - Experience with generative molecular design, candidate prioritization, or batch selection workflows. - Familiarity with causal inference, optimal experimental design, decision theory, or Bayesian methods. - Comfort working with frontier ML techniques where standard out-of-the-box approaches are insufficient. Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $228,000 - $358,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
ML Engineer, Applied AI
Your Impact at LILA We are growing our Applied AI org and seeking Machine Learning Engineers with expertise in model training, evaluation, and production-oriented ML systems. You’ll work on improving Lila’s AI models for customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated, and used in real customer contexts. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems. Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through experimentation, and ensure model behavior works well end to end inside the application. This role is ideal for someone who can bridge research and engineering: training or adapting models, building evaluation loops, debugging model behavior, and collaborating across AI and Software to move promising capabilities into production-quality systems. What You'll Be Building - Close the last-mile gap between Lila AI model capabilities and customer-specific scientific workflows. - Post-train models using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback. - Build evaluation loops that measure model quality, reliability, and customer fit. - Design experiments to improve model performance across applied customer use cases. - Feed customer learnings, data signals, and evaluation results back into the Lila AI model improvement cycles. - Partner with AI researchers to translate model improvements into usable capabilities. - Work with Software to integrate model behavior into end-to-end product workflows. - Debug model failures using traces, evaluations, customer context, and scientific feedback. - Build reusable tooling for model adaptation, evaluation, and deployment workflows. What You'll Need to Succeed - Experience building, training, adapting, or evaluating machine learning models. - Strong software engineering skills in Python and modern ML frameworks such as PyTorch, JAX, or TensorFlow. - Experience designing experiments, evaluation metrics, or test sets for model performance. - Ability to debug model behavior using data, traces, logs, and qualitative feedback. - Experience working across research and engineering teams to move ML capabilities into usable systems. - Familiarity with large language models, multi-modal models, or agentic AI systems. - Clear communication skills for translating customer needs into technical model improvements. Bonus Points For - Experience adapting models for customer-facing or production workflows. - Experience with scientific, technical, or data-intensive customer use cases. - Experience building evaluation harnesses, model monitoring, or quality dashboards. - Familiarity with retrieval-augmented generation, tool use, or agentic workflows. - Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL. - Experience training MoE architectures. - Experience working with product or customer-facing teams to translate needs into ML improvements. Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $116,000 - $170,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
ML Researcher - Image / Video Diffusion
ABOUT KREA At Krea, we are building next-generation AI creative tools. We're dedicated to making AI intuitive and controllable for creatives - our mission is to build tools that empower human creativity, not replace it. We believe AI is a new medium that allows us to express ourselves through various formats - text, images, video, sound, and even 3D. We're building better, smarter, and more controllable tools to harness this medium. We recently took this a step forward with the launch of Krea 2 https://www.krea.ai/blog/krea-2-image-model, our first foundation model, built completely from scratch for aesthetic diversity and stylistic control. We've raised over $83M and are backed by world-class investors such as a16z, Bain Capital, and Abstract. We work full-time and in-person at our waterfront office in San Francisco. We care about creativity: our team includes musicians, designers, visual artists, and engineers. We're looking for an experienced Researcher with engineering skills who can work on large-scale image and video models training experiments, with experience training image models at scale. OUR CULTURE - We work full-time and in-person at our North Beach office in San Francisco. - We believe that demonstrated interest in the creative space is key: our team includes musicians, designers, visual artists and more. - Fast iteration and execution speed. Bias towards action, agency, and independence. WHAT YOU'LL DO - Train diffusion models for image and video generation on large GPU clusters. - Fully optimize and profile large distributed training runs across model architectures, kernels, data loading, memory constraints, and communication. - Implement and improve various distributed training strategies including FSDP, CP, SP, TP, and EP. - Continuously improve model quality and reliability through data, model architecture, training pipeline, structuring experiments, and eval design. - Debug distributed training errors and implement fault tolerance solutions, identifying bad GPU, NVLink, Infiniband (IB) components as well as monitoring numerical errors and NCCL issues. - Ablate different architecture, attention, optimizer, data, and algorithmic choices to reliably improve efficiency and performance of our models. WHAT WE'RE LOOKING FOR - Proven track record in working with image or video models at scale (publications or open-source contributions a plus). - Strong proficiency in PyTorch and understanding of its inner workings. - Strong background in distributed training paradigms such as FSDP, CP, SP, USP, TP, and EP. Knowing how different parallelism strategies work together and their tradeoffs. - Experience in profiling and debugging large distributed training. Being comfortable with analyzing traces to identify bottlenecks and look for improvements. - Good knowledge of low precision training / inference in FP8, NVFP4, and MXFP8. - Solid understanding of diffusion model training pipeline across pretraining, midtraining, preference optimization, and reinforcement learning. - Keeping up with the developments in related fields such as LLM, VLM, representation learning, and robotics research. - Being comfortable working in a goal-oriented research environment. - Having good judgement around when one should explore different training strategies and when it's time to commit to a specific strategy to scale compute and data. - Comfortable working with underspecified goals. We expect every technical member to take an ambiguous research goal and break it down into concrete requirements, plans, experiment plan, and execution items. - Good research taste — bias towards simplicity and methods that scale well with compute, data, and minimal human supervision. - Ability to iterate rapidly, and propose creative research directions. - Be comfortable getting your hands dirty with data and designing custom data pipelines to improve data quality. WHAT WE OFFER - Team: Work alongside a world-class team building the future of AI creative tooling - Impact: Significant scope and company-wide impact - Competitive compensation: generous salary & equity packages - Health & wellness: 100% health & 99% dental/vision insurance premiums covered for employees, health FSA accounts, & long-term disability coverage - Time off: Flexible PTO policy - Financial planning: 401k with a 4% company-sponsored match - Meals in the office: breakfast, lunch, dinner - you name it, we'll cover it - Transit: Ubers covered to & from the office - Sponsorship: We're open to sponsoring international visas where we can (e.g., STEM OPT, OPT, H-1B, O-1, E-3). - And more! Please note the above benefits & perks are for full-time employees
Principal Engineer, Agentic AI Systems
About Inflection AI Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We’re shaping the future of AI by combining emotional intelligence (EQ) and raw intelligence (IQ) to elevate people’s potential. Inflection AI created Pi, the world’s first emotionally intelligent AI, to help people work through decisions, emotions, and challenges. Pi is a personal AI agent powered by Inflection AI’s foundation model, proving that AI can be personal, empathetic, and contextually aware. About the Role We're looking for a hands-on Principal Engineer to lead the architecture and technical direction of Inflection's agentic AI systems. You'll build production AI agents that can reason, retrieve context, use tools, and safely execute complex workflows while driving the design of scalable agent architectures, evaluation frameworks, observability, and reliability systems. Partnering closely with product, design, and engineering, you'll turn ambiguous problems into AI-native experiences, mentor senior engineers, and help establish the technical standards that power the next generation of intelligent agents. What You’ll Do - Build production AI agents that can plan, retrieve context, call tools, take actions, and recover from failures. - Design agent architectures for task decomposition, tool routing, memory, state management, human approval, and safe execution. - Create evals and observability systems for multi-step task success, tool-call accuracy, hallucination resistance, latency, cost, and failure modes. - Integrate agents with APIs, databases, SaaS tools, enterprise systems, internal systems, and user-facing workflows. - Improve reliability through structured outputs, guardrails, fallback paths, monitoring, permissions, auditability, and human-in-the-loop controls. - Partner with product, design, and engineering teams to turn ambiguous user problems into shipped AI-native features. - Make pragmatic model and infrastructure choices across proprietary models, open-source models, orchestration frameworks, vector databases, and cloud environments. - Contribute to technical direction, engineering best practices, and the quality bar for agentic AI systems. - Own the technical direction of agentic AI systems, provide support and coaching to the team. What We're Looking For - 10+ years of engineering experience, including 3+ years at staff/principal or equivalent scope, and 2+ years building production-grade agentic or LLM-powered systems used by real customers. - Strong Python or TypeScript experience. - Experience building LLM-powered products, AI agents, RAG systems, automation workflows, or model-integrated applications. - Strong backend engineering fundamentals, including APIs, databases, queues, testing, observability, cloud infrastructure, and deployment. - Experience with tool/function calling, structured outputs, embeddings, retrieval, prompt design, and model evaluation. - Strong product judgment and ability to move from ambiguous problem to shipped product quickly. - Ability to work effectively in a fast-moving environment with ownership, collaboration, and high technical standards. - Have a bachelor’s degree or equivalent in a related field to the offered position requirements Nice to Have - Experience with LangGraph, LlamaIndex, OpenAI, Anthropic, Hugging Face, vLLM, or similar tools. - Experience with agent memory, planning, multi-agent systems, browser automation, workflow automation, or AI copilots. - Experience building eval pipelines, synthetic test cases, observability, guardrails, or AI safety systems. - Experience with enterprise SaaS, productivity tools, developer tools, CRM, support, sales, finance, legal, or operations workflows. - Experience working in early-stage, high-growth, or zero-to-one product environments Employee Pay Disclosures At Inflection AI, we aim to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contributions to the company. For this role, Inflection AI estimates a starting annual base salary to fall within the range of $ 400,000.00 to $ 550,000.00 , depending on a candidate’s qualifications and level of experience. This role also includes a meaningful equity component, allowing employees to share in the long-term success of the company. Benefits Inflection AI values and supports our team’s mental, emotional, financial and physical health. We are focused on building a positive, safe, inclusive and inspiring place to work. Our benefits include: - Robust medical, dental and vision options with employer contributions for HSA, FSA and DFSA - 401k matching program - Flexible Time Off, 10 paid holidays, 5 days sick leave - Parental, Medical and Family care leave - Generous cell-phone, wellness and office set up stipends - Support of country-specific visa needs for international employees living in the Bay Area
Applied Research Engineer, Agents
ABOUT HEBBIA The AI platform for investors and bankers that generates alpha and drives upside. Founded in 2020 by George Sivulka and backed by Peter Thiel and Andreessen Horowitz, Hebbia powers investment decisions for BlackRock https://www.blackrock.com/us/individual/about-us/about-blackrock?cid=ppc:blk_us:corpaffairs_us_br_reputationmediamanagement_na_exact_ol:google:brand_nonprod:ol&gclsrc=aw.ds&gad_source=1&gad_campaignid=21584717446&gbraid=0AAAAACc6WDFbRb5bgB6zxVFLE_7yIy25I&gclid=CjwKCAiA9aPKBhBhEiwAyz82J2uDlcIPVsy0fhSZMS_rp_OsGerYzYFPFfLo4TlN8K4eCHWzPfvysRoC7oQQAvD_BwE, KKR https://www.kkr.com/, Carlyle https://www.carlyle.com/, Centerview https://www.centerviewpartners.com/, and 40% of the world’s largest asset managers. Our flagship product, Matrix, delivers industry-leading accuracy, speed, and transparency in AI-driven analysis. It is trusted to help manage over $30 trillion in assets globally. We deliver the intelligence that gives finance professionals a definitive edge. Our AI uncovers signals no human could see, surfaces hidden opportunities, and accelerates decisions with unmatched speed and conviction. We do not just streamline workflows. We transform how capital is deployed, how risk is managed, and how value is created across markets. Hebbia is not a tool. Hebbia is the competitive advantage that drives performance, alpha, and market leadership. THE TEAM Hebbia enables leading finance and legal firms with advanced reasoning, copiloting and retrieval capabilities - unlocking meaningful insights for meaningful use cases. The Agents team builds everything from core document understanding capabilities to co-piloting experiences for matrix and deep, multi-source research. We’ve built our own agentic frameworks powered by distributed systems built for scale. We don’t build for one-off success. We build steerable, reliable and explainable agentic systems. And we build these systems for the scale of data our customers bring to the table. Our goal is to unlock the unknowable unknown for customers all over the world. We want to build a product that becomes indisposable to our customers and as delightful as your favorite consumer product. We move fast and build first of their kind systems. WHAT WE’VE BUILT: 1. A custom multi-agent framework https://www.hebbia.com/blog/divide-and-conquer-hebbias-multi-agent-redesign powering everything from deep research capabilities to copiloting interfaces paired with distributed systems infrastructure for long running agentic tasks. 2. The world’s most powerful and scalable LLM inference engine - a distributed, asynchronous DAG orchestrator capable of incorporating live graph mutations, cooperating in tandem with our LLM throughput management capabilities. https://www.hebbia.com/blog/maximizer-hebbias-distributed-system-for-high-scale-llm-request-scheduling 3. Elastically scaling data representation and metadata generation → powering the most effective private data retrieval systems. 4. Industry leading agents https://www.hebbia.com/blog/goodbye-rag-how-hebbia-solved-information-retrieval-for-llmssolving problems from buy side company diligence to multi billion dollar M&A. THE ROLE As an Applied Research Engineer, you will be the bridge between research, industry, and application shaping the future of our core natural language processing systems. You will be responsible for enabling agentic capabilities across the Hebbia product suite. You will own experiments and POCs focused on combining the latest research findings with specific high value problems that our customers encounter each and every day. You will leverage our deep relationships with foundation model providers - partnering to beta test models, experiment with new features, and develop guidance on relative model strengths This role requires prior expertise in NLP, machine learning systems, and LLM evaluation; experience building with foundation models and experience working with Attention based NLP models is a plus. This role is best suited for an individual who can excel at both running experiments with novel LLM techniques as well as building production grade, LLM-enabled software systems - embedding directly in the software development lifecycle. RESPONSIBILITIES - Focused on LLMs, you will play a crucial role in analyzing and interpreting complex data types to derive and implement cutting edge insight generation systems. - Iterate and explore new LLM and NLP techniques maintaining our foothold as an industry leader - You will utilize your expertise in statistics, programming, and machine learning to develop and deploy data-driven models and algorithms - Your work will contribute to solving business problems, improving processes, and enhancing the overall performance of the company - Collaborate with cross-functional teams to improve NLP/LLM capabilities in app - Stay up-to-date with the latest advancements and research in the space - Collaborate with software engineers to integrate agentic capabilities into existing systems or develop new applications - Ensure that systems are efficient, maintainable and well monitored. - Iterate on validation and testing frameworks. WHO YOU ARE - Bachelor's degree in Computer Science, Engineering, or related field - Master’s degree in Computer Science, Mathematics, Machine Learning or a related field is a plus. - 7+ years software development experience at a venture-backed startup or top technology firm, with a focus on applied machine learning systems - Strong programming skills in Python - Experience with NLP and text processing libraries such as NLTK, SpaCy, or Apache Tika - Experience with Search and Indexing technologies - Proficient in machine learning techniques and algorithms - Experience working with foundational models and corresponding APIs - Knowledge of statistical analysis and data scraping techniques - Prior experience in developing NLP models and systems - Experience with prompting and building LLM applications and agents is a plus. - Excellent problem-solving and analytical skills - Strong communication and teamwork abilities - Strong capability to translate research into production software systems - Bonuses: - Experience building agentic systems or LLM enabled products. - Frequent user of AI products, especially during the development lifecycle (i.e. Cursor, Claude Code, etc). COMPENSATION The salary range for this role is $160,000 to $300,000. This range may be inclusive of several career levels at Hebbia and will be narrowed during the interview process based on the candidate’s experience and qualifications. Adjustments outside of this range may be considered for candidates whose qualifications significantly differ from those outlined in the job description. LIFE @ HEBBIA PTO: Unlimited Insurance: Medical + Dental + Vision + 401K Eats: Catered lunch daily + doordash dinner credit if you ever need to stay late Parental leave policy: 3 months non-birthing parent, 4 months for birthing parent Fertility benefits: $15k lifetime benefit New hire equity grant: competitive equity package with unmatched upside potential #LI-Onsite
Member of Technical Staff, Post-Training
ABOUT HANDSHAKE Handshake's mission is to organize expert human knowledge to advance the AI economy. Handshake AI works directly with frontier labs on their most consequential data, evaluation, and post-training challenges, building the systems that turn expert human knowledge into the data and evaluations that make frontier models better. You will work alongside engineers, researchers, operators, and builders from organizations including Scale AI, Meta, Google, Amazon, xAI, Notion, and Palantir—and help build the systems that make expert human knowledge useful for advancing AI. ABOUT HANDSHAKE LABS Handshake Labs is building external AI products, research platforms, and customer-facing AI systems. We are evolving work that is often custom-built for an individual partner into reusable products and platforms that improve with every deployment. Our work spans the full post-training loop: designing evaluations and training environments, building high-quality data and feedback systems, running experiments, and turning what works into durable infrastructure. For example, we are developing agents that can analyze long, complex coding-agent sessions in days rather than weeks—with expert review and calibration built into the system. THE ROLE We are hiring a Member of Technical Staff, Post-Training to help define and build this new organization. This is a broad, high-ownership role for researchers who build. You may come from research science, research engineering, machine learning engineering, or a closely related background; what matters is the ability to reason deeply about model improvement and turn that reasoning into reliable systems. You will partner with researchers, domain experts, and customers to turn ambiguous post-training questions into experiments, evaluation frameworks, data pipelines, and products. Early members of the team will have unusual influence over our technical direction, operating culture, and the reusable systems we build. We care more about demonstrated research capability, technical judgment, and a builder’s mindset than a specific title, degree, or career path. Location: San Francisco & Mountain View preferred; we are open to exceptional candidates in other locations. WHAT YOU’LL DO - Design post-training systems and methodologies for frontier models, including supervised fine-tuning, reinforcement learning, preference optimization, reward modeling, and related approaches. - Translate open-ended research or partner needs into clear hypotheses, experiments, evaluation plans, and production-quality implementations. - Build and improve evaluation frameworks, benchmarks, training environments, data-processing pipelines, and quality-control systems. - Run fast, rigorous iteration loops: prototype, evaluate, interpret results, and turn learnings into the next system or product. - Partner directly with AI researchers and domain experts to develop high-signal data, feedback, and evaluation methods. - Identify repeatable patterns across engagements and productize them into reusable software and platforms. - Raise the technical bar through strong design judgment, clear communication, code quality, and mentorship. - Contribute to the field through benchmarks, open-source tools, research, and technical writing where it creates leverage. WHAT WE’RE LOOKING FOR - 3+ years of demonstrated strength in post-training, fine-tuning, or model-evaluation work. Relevant experience may include RL, SFT, LoRA/PEFT, full fine-tuning, RLHF, DPO, PPO, reward modeling, or training environments. - Strong Python skills and the ability to write clean, efficient, scalable software. - Hands-on experience with modern ML tooling, particularly PyTorch and large-scale data, training, or evaluation workflows. - Sound experimental judgment: you can form hypotheses, choose meaningful metrics, diagnose failures, and distinguish signal from noise. - Experience designing systems—not only implementing specifications—including the ability to make tradeoffs around quality, scale, reliability, and reuse. - Comfort operating in an ambiguous, fast-moving environment with substantial ownership. - Collaborative, low-ego communication and the ability to work effectively with researchers, engineers, domain experts, and customers. ESPECIALLY COMPELLING EXPERIENCE - Building or operating large-scale ML training, inference, data, or evaluation systems. - Developing LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks. - Research or applied work on reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems. - Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems or AI research. - Experience productizing research or repeated customer work into robust, reusable platforms. WHY JOIN - Work on problems at the center of how frontier AI systems improve, alongside leading labs and domain experts. - Help build an early technical organization where your work shapes the roadmap, standards, and culture. - Move fluidly from research insight to real-world systems, with the resources and customer context to see those systems matter. - Join a company building durable infrastructure for careers in the AI economy. PERKS Handshake delivers benefits that help you feel supported—and thrive at work and in life. The below benefits are for full-time US employees. 🎯 Ownership: Equity in a fast-growing company 💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching 🍼 Family Support: Paid parental leave, fertility benefits, parental coaching 💝 Wellbeing: Medical, dental, and vision, mental health support, $500 wellness stipend 📚 Growth: $2,000 learning stipend, ongoing development 💻 Office: Commuting support, free lunch, and gym in our SF office 🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days 🤝 Connection: Team outings & referral bonuses
Machine Learning Engineer, Assistant Quality
About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: Glean is seeking a Machine Learning Engineer to improve the quality of our AI Assistant and autonomous agents. This role sits at the intersection of production machine learning, LLM-powered systems, and product engineering, with a focus on building, evaluating, and iterating on assistant experiences that are useful, reliable, and grounded in real enterprise workflows. You will work on applied problems across agent quality, evaluation, personalization, retrieval, and orchestration. The ideal person is excited by shipping production systems, not pure research, and wants to help shape how Glean’s assistant gets better over time through stronger signals, tighter feedback loops, and better end-to-end execution quality. You will: - Build and improve ML and LLM-powered systems that raise the quality of Glean’s AI Assistant and autonomous agents across real user workflows. - Design evaluation, benchmarking, and monitoring loops to measure assistant quality, model quality, and end-to-end system performance. - Develop and iterate on signals, prompts, workflows, and model-driven logic that improve reasoning, planning, personalization, and task completion quality. - Work across areas such as RAG, semantic search, recommendation-style systems, post-training or reinforcement learning, and agent orchestration where they materially improve product outcomes. - Partner closely with product, design, and engineering teammates to understand customer pain points and ship high-quality production systems quickly. - Contribute to the data and ML infrastructure needed to support robust experimentation, offline and online evaluation, and continuous model improvement. About you: - 2+ years of industry experience in machine learning, applied AI, or software engineering with significant ML ownership. - Strong hands-on coding ability and a track record of shipping production systems, not just prototypes or research projects. - Experience in one or more of the following areas: LLM applications, NLP, search, retrieval, recommendations, evaluation frameworks, agent systems, or personalization. - Comfort working across both modeling and product engineering details, including experimentation, quality measurement, and production iteration. - Proficiency in common ML tooling and strong software engineering fundamentals in languages such as Python, Go, Java, or C++. - A pragmatic, product-minded approach. You know when to use sophisticated ML techniques and when simple, reliable systems are the better answer. - A proactive, low-ego working style and excitement about learning quickly in a high-velocity environment. Location: - This role is hybrid (4 days a week in our San Francisco office) Compensation & Benefits: The standard base salary range for this position is $180,000 - $205,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused. We’re committed to building and sustaining a diverse, inclusive workplace. We strive to attract and retain people with a wide range of backgrounds, experiences, and perspectives, and we do not discriminate on the basis of gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race. LI-HYBRID AI-First Mindset at Glean: At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy . By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement . By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement , and I agree to the terms.
Research Engineer (New Grad)
We are Genmo, a research lab developing the world’s most sophisticated video world models to understand, simulate, and interact with the physical world. Our mission is to unlock the right brain of AGI. Join us in advancing physical intelligence and enabling robots to learn and act in a changing world. ABOUT THE ROLE We're seeking an exceptional Software Engineer to join our research team in advancing the frontiers of visual generative AI. As a Research Engineer, you'll work alongside experienced researchers to develop and improve generative models while ensuring their safe and effective deployment. WHAT YOU'LL DO - Contribute to the development of novel machine learning techniques for visual generative modeling - Implement and experiment with various generative architectures (Diffusion Models, GANs, Transformers) - Help optimize model performance and scaling capabilities - Assist in ensuring safe and robust model deployment - Collaborate with cross-functional teams to integrate research innovations - Participate in research discussions and contribute to technical documentation REQUIRED QUALIFICATIONS - BS or MS in Computer Science, Machine Learning, or related field (recent graduates welcome) - 2+ years of experience in Machine Learning - Strong foundation in machine learning, particularly generative models - Solid programming skills in Python and PyTorch/TensorFlow - Experience with deep learning frameworks and model training - Understanding of fundamental concepts in computer vision and generative AI - Project experience (academic or personal) with generative models - Clear communication skills and ability to work in a research team PREFERRED QUALIFICATIONS - Research projects or thesis work in generative AI - Experience with different types of generative models: - Diffusion Models - GANs - Transformer architectures - Large Language Models - Familiarity with distributed computing and GPU optimization - Contributions to open-source ML projects - Academic publications or workshop papers - Experience with ML infrastructure and deployment This position is onsite at our San Francisco office (4–5 days per week). We are a fast-paced startup, and our team works very hard to build impactful products quickly. We’re looking for equally driven and hardworking individuals who thrive in an environment where learning is constant, challenges are exciting, and collaboration is key. Genmo is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Genmo, Inc. is an E-Verify company and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1urlF3XKQTw83aJfpMKMwrUP1E1mUlNTO/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1ZD4zax6Li8oTN_VHd9w3rpFPoWeaeGVO/view?usp=drive_link.
Senior Machine Learning Scientist I/II, Model-Driv...
The Role: Generate:Biomedicines is seeking a creative, rigorous, and execution-oriented machine learning scientist to join our Model-Driven Design team. This role will focus on building the ML methods, data strategies, and closed-loop systems that determine what we design, build, test, and learn from next. The Model-Driven Design team works at the interface of machine learning, protein design, engineering, and experimental science. We develop and apply models and quantitative frameworks that help Generate discover and optimize therapeutic proteins. In this role, you will help advance the technical foundation of our lab-in-the-loop protein optimization platform, with a focus on sequential decision-making, experimental design, property modeling, and scalable design systems. We are looking for someone who can serve as a technical leader and hands-on individual contributor, driving complex, high-impact work from problem framing through implementation, deployment, and experimental impact. The ideal candidate combines depth in probabilistic machine learning, Bayesian optimization, active learning, or related approaches with the practical judgment and engineering discipline to turn technical ideas into reliable systems that drive impact. You will partner closely with protein designers, wet-lab scientists, ML scientists, and engineers to build durable capabilities that accelerate therapeutic discovery. This role is part of a highly collaborative team environment that balances in-person collaboration with hybrid flexibility based out of our Somerville, MA office. Here's how you will contribute: - Develop new machine learning methods and systems for lab-in-the-loop protein optimization, including property models and multi-objective optimization strategies for therapeutic protein design. - Shape data-generation and data-use strategies that make experimental campaigns maximally informative for model improvement, therapeutic optimization, and future design cycles. - Build and apply LLM-enabled and agentic workflows that help scientists explore design hypotheses, connect models to data and experiments, and accelerate iterative learning. - Design, implement, test, and maintain production-quality ML models, software components, and data workflows, with attention to reliability, reproducibility, observability, and computational efficiency. - Partner with ML engineering and software teams to integrate these components into robust, scalable platform capabilities, with clear ownership across team boundaries. - Collaborate closely with protein designers and wet-lab scientists to ensure models and optimization systems are grounded in experimental reality and deliver measurable impact. - Identify important technical gaps, develop proposals, define milestones, align stakeholders, and help set technical direction across cross-functional programs. - Communicate clearly across disciplines and help raise technical standards across ML, engineering, protein design, and experimental teams. The Ideal Candidate will have: - PhD in machine learning, computational biology, computer science, applied mathematics, engineering, or a related quantitative field. - Strong practical experience with probabilistic machine learning, Bayesian optimization, active learning, experimental design, or related approaches for sequential decision-making under uncertainty. - Experience developing machine learning methods or systems for biological, biomedical, or experimental scientific data, with an ability to reason about noisy assays, sparse labels, experimental bias, and data-generation strategy. - Demonstrated ability to translate ML ideas into systems, tools, or workflows that affect real scientific, experimental, or product decisions. - Strong Python skills and experience with modern ML frameworks such as PyTorch, JAX, or similar tools. - Strong systems thinking and ability to design technical interfaces, reason about system tradeoffs, and partner with engineering teams to build scalable, maintainable ML infrastructure. - Excellent communication skills and ability to bridge ML, engineering, protein design, and experimental stakeholders. - Pragmatic, collaborative working style, with the ability to bring structure to open-ended problems and balance scientific rigor with execution in fast-moving, cross-functional environments. Nice to have - Experience in protein design, protein engineering, antibody engineering, biologics discovery, or drug development. - Experience partnering with experimental teams on design-build-test-learn cycles, high-throughput screening, directed evolution, pooled libraries, or model-guided experimental campaigns. - Experience with multi-objective optimization, uncertainty calibration, model-guided library design, or experimental campaign planning. - Experience developing and applying deep learning models, including transformer-based architectures - Experience building or applying LLM agents, scientific copilots, or agentic systems in technical workflows. - Experience contributing to shared ML platforms, libraries, APIs, or developer tooling, including monitoring, debugging, performance optimization, and long-term maintenance. Who Will Love This Job: This is an opportunity to shape how machine learning is used to make better decisions across the full protein design cycle. You will work on problems where models, data, experiments, and engineering systems are tightly connected, and where better optimization strategies can directly change what gets built and tested in the lab. You will join a collaborative, ambitious team working to build a platform for therapeutic protein design that learns continuously from experimental data and turns that learning into new and better therapeutics with real impact. About Generate Biomedicines We are a clinical-stage generative biology company pioneering the AI revolution in drug design and development. We are advancing a new approach to drug creation—one grounded in the ability to design proteins with defined biological intent. By integrating machine learning with large-scale experimentation, this approach aims to reduce the uncertainty, time, and cost associated with developing protein-based medicines. Founded in 2018, we are advancing a growing pipeline of clinical and preclinical programs across multiple disease areas and protein modalities. By unifying computational design and clinical development within a single operating model, we translate this approach into clinical-stage programs and are leading a shift from traditional drug discovery toward systematic drug generation. At Generate:Biomedicines, we collaborate across disciplines in new ways to invent and innovate. We bring diverse perspectives to a shared goal of delivering better medicines to patients in need, faster, guided by our values and leadership behaviors. Generate:Biomedicines is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Recruitment & Staffing Agencies : Generate:Biomedicines does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Generate:Biomedicines or its employees is strictly prohibited unless contacted directly by the Company’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Generate:Biomedicines and the Company will not owe any referral or other fees with respect thereto. LI-HM1 Compensation: The base salary range provided reflects our current estimate of what we anticipate paying for this position. Your actual base salary will be based on several factors, including job-related skills, experience, internal equity, relevant education or training, and market dynamics. In addition, you will be eligible for an annual bonus, equity compensation, and a competitive benefits package. Per Year Salary Range $192,000 - $265,000 USD
Software Engineer, AI Model Enablement & Inference
About FuriosaAI FuriosaAI builds high-performance, high-efficiency AI compute for the Inference Era. Founded in 2017 by veteran semiconductor and AI algorithm engineers, Furiosa operates globally with offices in Korea and Silicon Valley, along with a compiler-focused R&D lab in Lisbon. Our vision is to make AI computing sustainable, enabling access to powerful AI for everyone on Earth. We solve the AI hardware energy and operational cost crisis at the architectural level, rather than through brute force, building the world's first truly AI-native compute platform to unlock the full potential of artificial intelligence for every enterprise. About the Job FuriosaAI is seeking a Software Engineer to join our MLSys team. We apply deep expertise in model architectures and algorithms to build efficient, production-ready implementations of frontier LLMs tailored to our Tensor Contraction Processor (TCP) architecture. Our work makes these models available through FuriosaAI's software stack, enabling developers to deploy them efficiently on our NPUs with Furiosa-LLM (FLM). You will develop correct, efficient model kernels in Tensor Contraction Language (TCL), FuriosaAI's Python-based DSL for tensor computations, and work closely with the Inference Engine and Compiler teams on kernel implementation strategies and FLM integration. Key Responsibilities - Analyze model architectures, algorithms, and reference implementations to identify new model features and document their implementation requirements and trade-offs. Work with the Inference Engine and Compiler teams to agree on model implementation and integration strategies. - Design, implement, and optimize model-specific TCL kernels, including attention and mixture-of-experts computations, for high performance and efficient use of NPU resources. - Integrate new models and TCL kernels into Furiosa-LLM to enable correct, efficient inference. - Improve the process for adding new models by building reusable analysis and integration tools, automating validation and benchmarking, and documenting repeatable workflows. - Validate kernel and model correctness on NPUs against reference implementations, investigate numerical differences, and build regression tests for supported configurations. - Evaluate techniques from frameworks such as vLLM and SGLang, adapt and apply relevant approaches to TCL kernels and Furiosa-LLM integration, and document validated findings. - Continuously study state-of-the-art models to deepen expertise in model architectures and algorithms. Share insights across the company and provide technical guidance to teams on model capabilities, architectural trade-offs, and inference requirements. Minimum Qualifications - Deep understanding of transformer-based LLMs, including attention variants, mixture-of-experts architectures, and KV-cache behavior. - Strong Python programming skills and hands-on experience reading, modifying, and debugging model implementations in PyTorch or a comparable framework. - Hands-on experience implementing, debugging, and optimizing tensor operations or accelerator kernels, with an understanding of compute, memory, and numerical correctness. - Understanding of LLM inference performance, including prefill/decode, batching, and latency–throughput trade-offs, with experience in quantitative performance evaluation. - Ability to read and reason about Rust or C++ code when working on inference systems. - Clear technical communication and cross-team collaboration skills, including the ability to turn analysis into actionable engineering decisions. Preferred Qualifications - Experience bringing up or optimizing models on GPUs, NPUs, TPUs, or other AI accelerators. - Familiarity with the internals of vLLM, SGLang, TensorRT-LLM, or similar inference frameworks, including scheduling, caching, or model parallelism. - Familiarity with ML compilers and optimizations such as fusion, tiling, and scheduling. - Experience developing and optimizing accelerator kernels using CUDA, Triton, or a tensor DSL. - Experience developing software in Rust, building model validation or benchmarking tools, or contributing to open-source model and inference projects. Why Join FuriosaAI The defining bottleneck of the AI era is building the right hardware and software stack to run it at global scale. Furiosa is solving this challenge holistically from the ground up. With our flagship chip, RNGD, in mass production today and our next-generation platform in development with Broadcom, we are proving that full-stack, tensor-native compute is the future of AI infrastructure. This is a pivotal moment to join our team, right as we accelerate our global expansion. At Furiosa, you will: Solve AI’s Most Urgent Challenge. Help build the high-performance, energy-efficient inference hardware and software required to fulfill the promise of advanced AI. Pioneer Full-Stack Co-Design. Work with teams that are architecting solutions from silicon up through the compiler (featuring innovations like Tensor Contraction Language and Virtual ISA) and serving frameworks. Ship Real-World Silicon, Software, and Solutions. Turn breakthrough technology into commercial deployment. RNGD is in mass production with TSMC and running live enterprise workloads for global leaders like LG AI Research and Samsung SDS. Partner With the Industry's Best. Collaborate across an elite global ecosystem that includes TSMC, Broadcom, SK Hynix, and GUC. Do Your Life’s Best Work. Join a brilliant, low-ego, mission-driven team in a high-trust environment that values autonomy, intellectual curiosity, and shared ambition. Contact - recruit@furiosa.ai
Machine Learning Engineer: Imitation and Reinforce...
JOIN THE TEAM BRINGING ADVANCED AUTONOMY TO THE BUILT WORLD At Bedrock, we're moving AI out of the lab and into the real world. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction equipment across the country, improving safety on job sites and accelerating schedules on critical infrastructure projects. We're not here debating the future of AI. We're deploying it in the real world. In just two years, we've raised $350M and achieved the first fully autonomous excavator deployments in construction. This is where algorithms meet steel-toed boots. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to do meaningful work on hard problems, we'd love to have you join us. The Mission: We’re looking for a Machine Learning Engineer with a focus on behavior learning, specifically data-driven behavior policies and robust data infrastructure. In this role, you'll be responsible for developing and scaling state-of-the-art learning architectures, while also building the data systems that make these models reliable, scalable, and reproducible in production. What You’ll Do: - Design, train, validate, and launch models for behavior cloning and reinforcement learning - Build and maintain data ingestion, labeling, and management pipelines to ensure high-quality training datasets - Build metrics to evaluate model performance in open loop, simulation, and in the real world - Collaborate with simulation, systems, and infrastructure teams to integrate ML models into real-world autonomous systems - Deploy and debug these models in real-world environments, addressing practical issues such as latency, hardware constraints, and system integration What We’re Looking For: - 3+ years of practical experience applying Machine Learning with Deep Learning frameworks, such as PyTorch/Tensorflow/JAX to solve real-world problems - 3+ years of professional experience building, deploying, and maintaining Machine Learning models in production environments - Familiarity with recent literature and methods in learned behavior policies - Practical experience in behavior cloning and/or reinforcement learning - Bonus: Experience with diffusion policies, Vision-Language-Action (VLA) models, or related technologies - Bonus: Published work in conferences such as ICRA, IROS, CoRL, CVPR, ECCV, ICCV, ICML, NeurIPS, … Bedrock Robotics is an Equal Opportunity Employer We’re committed to building a diverse and inclusive workplace. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, age, disability, veteran status, genetic information, or any other protected characteristic. Reasonable Accommodations We want our hiring process to be accessible to everyone. If you need an accommodation to participate in the application or interview process, please let your recruiter know so we can support you.
Staff AI Infrastructure Engineer
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic systems. The team is responsible for taking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product. The team also develops Lattice for Mission Autonomy, Anduril’s premier software platform that enables masses of Fury, Barracuda, and other first and third party robots to collaborate across various missions. We work in close coordination with specialist teams like Perception, Motion Planning, Hardware, and Test Engineering to solve some of the hardest problems facing our customers. We are looking for software engineers and roboticists excited about creating a powerful autonomy software stack that includes computer vision, motion planning, SLAM, controls, estimation, and secure communications. ABOUT THE JOB We are looking for a founding Staff AI Infrastructure Engineer to architect, build, and scale the end-to-end machine learning platform that powers Anduril’s autonomous systems. As a Staff Engineer, you will own the technical roadmap for our ML platform. You will build the robust infrastructure, MLOps tooling, and systems architecture required to train, evaluate, host, and serve complex AI models (including LLMs, computer vision, and RL agents) in both cloud environments and air-gapped, offline tactical edge networks. You will be a force multiplier for our AI Research Scientists, optimizing their experimentation velocity and managing the lifecycle of terabytes of multi-modal sensor and simulation data. Over time, you will help recruit, mentor, and expand this infrastructure engineering team. WHAT YOU’LL DO - Design, build, and maintain our foundational training, orchestration, and experimentation infrastructure to support state-of-the-art model development. - Actively identify, measure, and eliminate bottlenecks in the ML research lifecycle. Build highly automated tools for hyperparameter tuning, model profiling, and experimentation tracking. - Design and scale robust, high-performance ETL pipelines capable of processing terabytes of multi-modal data (video, camera feeds, radar, flight telemetry, and simulation logs) captured from physical assets and test sites. - Architect high-throughput, low-latency model serving frameworks optimized for both scalable cloud environments and air-gapped, resource-constrained tactical edge environments. Build CI/CD pipelines for ML models with automated validation, canary deployments, and rollback capabilities. - Build robust, automated pipelines for continuous evaluation, model validation, and reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in high-stakes environments. - Work closely with AI Researchers, Computer Vision teams, and platform engineers to design unified infrastructure standards across the company's autonomous systems programs. REQUIRED QUALIFICATIONS - 7+ years of software engineering experience with a proven track record of designing, building, and operating production-scale machine learning systems and platforms (MLOps). - Proficient in Python, Go, C++, or similar backend languages. Deep understanding of ML systems design, memory management, and distributed computing. - Deep experience with containerized deployments (Docker, Kubernetes), GPU scheduling/orchestration, and distributed training frameworks (e.g., PyTorch Distributed, Ray, Slurm, or Megatron-LM). - Hands-on experience building distributed data pipelines (ETL) and managing massive datasets (terabytes of unstructured/multi-modal sensor data). - Experience setting technical direction, leading complex system migrations, and mentoring senior engineers. - Eligible to obtain and maintain an active U.S. Top Secret security clearance. PREFERRED QUALIFICATIONS - Experience building and running ML infrastructure, model serving, or software registries within secure, air-gapped, or highly regulated environments (e.g., IL5/IL6, GovCloud). - Experience specifically building training and evaluation platforms for Large Language Models, Generative AI architectures, or Reinforcement Learning (RL) pipelines. - Experience profiling training hardware performance, identifying bottlenecks across networks and memory, and optimizing hardware utilization. - Experience designing and operating multi-tenant ML platforms that serve multiple research teams, with robust resource isolation, quota management, and fair scheduling across shared GPU clusters. - Hands-on experience with next-generation AI accelerators beyond standard GPUs (e.g., AWS Trainium, Google TPUs, or custom ASICs) for training and inference workloads. - Experience building production monitoring and observability systems for ML models, including prediction drift detection, data quality monitoring, and automated retraining triggers. US Salary Range $220,000 - $292,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Agentic AI Engineer, Automation
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. About the team Air Dominance & Strike designs, builds, and flies autonomous air vehicles — from collaborative combat aircraft to expendable cruise missiles to counter-UAS interceptors. Our vehicles move from whiteboard to first flight on timelines that traditional primes consider impossible, which means our design cycles live or die on how fast we can close the iteration loop and begin testing. The Anduril AI-Engineering team exists to collapse that loop. We are engineers first. We work from engineering first principles and unlock capability through machine learning. We are building to scale across design, analysis, test, and program execution, with agent pipelines and tooling that carry across programs. About the job We are looking for an Agentic AI Engineer to automate engineering workflows, build new capability through agents, and improve how engineers access and act on their data. Our R&D work runs through tools of varying fidelity, from empirical methods and low-order models to high-fidelity solvers. You will chain those tools into automated flows that carry a design through analysis to build and then back into the next iteration. You will own agent pipelines end to end: orchestration logic, tool and data integrations, evaluation harnesses, and guardrails. Much of the work is getting agents to drive legacy engineering software which were built for human operators rather than programmatic control. You will also use agents to build new tools as programs evolve and requirements change, standing up capability on program timelines rather than software release cycles. Defense experience is not required. We are looking for engineers who came to machine learning through the problems they were already trying to solve. This role is based onsite in our Costa Mesa, CA office. What You'll Do - Build and deploy multi-agent pipelines that compress the engineering design loop, including automated case setup, batch submission, and post-processing of solver runs across CFD, FEA, thermal, and electromagnetics - Design, implement, and evolve how agents interact with classical engineering software: tool calling, prompt engineering, task decomposition, state management, retries, and human-in-the-loop checkpoints, - Automate data extraction and aggregation across solvers, test benches, and program systems, and build the dashboards that give engineers and leadership rapid access to their own results - Build bespoke tooling through agents for engineering sub-disciplines, including aerodynamics, thermal, GNC, structures, and avionics, that brings new capability in-house - Choose which model to run at each stage of agent work, from planning through tool development to execution, and tune routing, context management, and caching so agents run efficiently against token cost and latency budgets - Build the evaluation harnesses, guardrails, and sandboxed execution needed to defend agent behavior before it is deployed on a program - Mentor engineers who are not ML specialists, and work with them to identify and scope the projects where agents deliver the highest impact Qualifications - MS or PhD in aerospace, mechanical, or electrical engineering, computer science, data science, or machine learning - 0-3 years of professional experience, with demonstrated hands-on work implementing agentic AI systems - Strong programming skills in Python and MATLAB, and working knowledge of at least one additional core language such as Java, Go, or C++ - Experience with Model Context Protocols, including building and maintaining MCP servers - Experience building agentic systems, including multi-agent orchestration, tool calling, prompt engineering, integration into classical software systems and retrieval-augmented generation - Experience with data extraction, aggregation, and sanitization techniques, and with building production data pipelines from heterogeneous engineering and test sources - Proficiency developing on Linux, with containerized deployment via Docker and Kubernetes - Eligible to obtain and maintain a U.S. Secret security clearance Preferred Qualifications - Experience deploying LLM applications in accredited or otherwise restricted cloud environments - Experience with cost and latency optimization at scale, including caching, batching, and prompt and context efficiency - Experience with structured output, function calling, and prompt optimization at production scale - Experience building knowledge graphs or semantic layers over engineering data - Familiarity with engineering toolchains such as PLM systems, requirements management tools, solvers, and test data systems Per Anduril's expansion plans, we anticipate this job will be based out of Long Beach, CA beginning late 2027. US Salary Range $220,000 - $292,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.
Senior Machine Learning Engineer
About Us: Here at Ambience, we never set out to be just another scribe. We’re building the AI intelligence platform that restores humanity to healthcare and drives meaningful ROI for health systems across the country. Our technology helps providers focus on delivering great care by removing the administrative burden that pulls them away from patients and away from their most impactful work. Ambience delivers real-time coding-aware documentation and clinical workflow support across ambulatory, emergency and inpatient settings at the top health systems in North America. Our teams operate relentlessly with extreme ownership to build the best solutions for our health system partners. We value candor, positivity and deep thought — and we expect a lot from each other because we know the problems we’re solving truly matter. Ambience was ranked #1 for Improving the Clinician Experience in the KLAS Research Emerging Solutions Top 20 Report, recognized by Fast Company as one of the Next Big Things in Tech, named one of the best AI companies in healthcare by Inc., and selected as a LinkedIn Top Startup in 2024 and 2025. We’re backed by Oak HC/FT, Andreessen Horowitz (a16z), OpenAI Startup Fund, and Kleiner Perkins — and we’re just getting started. THE ROLE: As a Senior Machine Learning Engineer at Ambience, you will build and improve the AI systems that power our clinical products. You’ll own complex projects end-to-end, from diagnosing production failures and designing evaluations to building, deploying, and iterating on model and agentic systems. This is a highly hands-on role with significant technical ownership. You’ll work closely with clinicians, product managers, and fellow engineers to translate cutting-edge research into reliable, production-grade AI systems. Our engineering roles are hybrid — working onsite at our San Francisco office three days per week. WHAT YOU’LL DO: - Build Trustworthy AI Evaluation Systems: Design and own evaluation pipelines for LLM and agentic systems, combining automated graders, regression testing, production feedback, and human evaluation to measure real product quality. - Improve Production Model Behavior: Diagnose high-impact failure modes and test improvements across prompting, retrieval, context, routing, data, fine-tuning, or other model and system interventions. - Build Agentic AI Systems: Develop production systems involving tool use, retrieval, context and state management, routing, orchestration, tracing, and failure recovery. - Build Data and Improvement Flywheels: Turn production failures and user feedback into better datasets, evaluations, and model behavior through active learning and systematic iteration. - Stay at the Cutting Edge: Distill insights from recent research in LLMs, agents, NLP, speech, and multimodal AI and translate promising ideas into practical experiments. - Own AI Systems End-to-End: Work across models, data, evaluation, orchestration, serving, and observability, while remaining deeply hands-on in code and production debugging. WHO YOU ARE: - Strong Production AI Experience 5+ years in production ML, research engineering, or applied AI. Have built a consequential production AI system or materially improved model behavior in production. Strong understanding of modern LLMs, transformers, and production AI systems. - Deep Evaluation Experience Experienced designing evaluations for LLMs, agents, or other complex AI systems. Can turn ambiguous quality problems into measurable dimensions, datasets, and experiments. Familiar with challenges such as grader bias, leakage, misleading aggregate metrics, regression detection, and offline-online mismatch. - Agentic Systems Experience Experience building production systems involving multiple models, tools, retrieval, context, state, routing, or orchestration. Understands reliability and failure modes in complex AI workflows, not just individual model calls. - Production-Grade Software Engineer Proficient in Python and modern ML frameworks; PyTorch preferred. Comfortable with deployment, observability, CI/CD, and containerized systems. Still highly hands-on: writes code, inspects traces, analyzes failures, and debugs production systems. - Data-Centric AI Developer Skilled at building high-quality datasets and feedback loops. Experienced using production failures, user feedback, and active learning to improve model and system quality. - Effective Interdisciplinary Collaborator Able to work closely with clinicians, product managers, and fellow engineers. Strong communicator who can simplify complex AI concepts for diverse audiences. Comfortable owning ambiguous technical problems and driving them to measurable outcomes. Nice-to-Haves - Experience with realtime voice, conversational AI, or multimodal systems. - Experience with fine-tuning, post-training, or model adaptation. - Prior work in healthcare, clinical AI, or other regulated, high-stakes industries. - Experience interviewing or mentoring ML engineers. - Open-source contributions to ML, agent, or evaluation tooling. Life at Ambience Working at Ambience means opting into a high-ownership, high-trust environment built for people who want to grow fast, operate decisively and focus on work that matters. This could be the right place for you if you want to - Work on mission-critical AI technology that directly improves clinicians’ day-to-day lives and health system financial health across some of the most complex, high-stakes workflows in the world. - Join a “dream team” culture where we hire exceptional people, expect exceptional outcomes and invest deeply in feedback and continuous growth. We operate as a championship team, and that means being ok with hard, uncomfortable, ambiguous problems that lead to real greatness. - Operate with real ownership and accountability in an environment where there are no bystanders: If something is broken, we fix it! You will have meaningful autonomy and be expected to drive work to completion. To help you do your best work, we pair these expectations with benefits intentionally designed to help you feel supported and safe at Ambience and beyond. Some of our key benefits include - Comprehensive medical, dental, and vision coverage for you and your dependents - 401(k) with a company match of up to 3% of base salary - A remote-friendly culture (with a San Francisco HQ) and full equipment provisioning to ensure you can work effectively from wherever you’re based. - Parental leave to support your family needs - Annual company-wide off-sites, team off-sites and regular team lunches and all-hands gatherings, with travel, lodging and meals covered - Flexible time off with no annual cap, company-wide holidays and an annual holiday shutdown from December 24–January 1 designed to support real rest and long-term sustainability. Ambience Healthcare is an equal opportunity employer and is committed to building a diverse and inclusive workplace. We do not discriminate on the basis of race, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, disability, veteran status, genetic information, or any other legally protected status. We encourage applicants from all backgrounds to apply. Ambience is committed to supporting every candidate’s ability to fully participate in our hiring process. If you need any accommodations during your application or interviews, please reach out to our Recruiting team at accommodations@ambiencehealthcare.com recruiting@ambiencehealth.com. We’ll handle your request confidentially and work with you to ensure an accessible and equitable experience for all candidates. Ambience Healthcare has become aware of scams targeting jobseekers with fake jobs and even interviewing people. Our emails will always come from @ambiencehealthcare.com. We would never our ask candidates to download apps or make any form of payment(s). If you are contacted through WhatsApp, Telegram, similar but fake email domains, or asked to make a payment, these contacts are not legitimate. Report the issue immediately to LinkedIn and the FBI https://www.fbi.gov/how-we-can-help-you/scams-and-safety.
AI Staff Software Engineer, ML Platform
About Aidoc Aidoc is the market leader in healthcare AI and has developed the world's largest clinical frontier model, supporting nearly 50 million patients each year. Aidoc is deployed in more than 2,000 medical centers worldwide. Since our founding in 2016, Aidoc has raised over $500 million and achieved a record number of FDA-cleared solutions. Medical diagnosis is hitting a breaking point, where the number of physicians hasn't kept up with the rapidly growing patient load. This leads to misdiagnosis and treatment delays that result in hundreds of thousands of deaths every year. Aidoc assists clinicians by precisely highlighting diseases directly on medical images, preventing misdiagnosis and ensuring that urgent patients receive the immediate attention they need. About this role This is a Principal AI Software Engineer role at the heart of Aidoc’s AI Engineering group—where algorithms, infrastructure, and products intersect. You’ll lead the evolution and scaling of Aidoc’s AI architecture , ensuring reliability, efficiency, and impact across global clinical workflows. The role combines technical depth with cross-functional influence, strengthening how our AI systems are built, integrated, and delivered to drive ongoing impact for patients and care teams. Responsibilities - Lead cross-domain engineering initiatives connecting AI algorithms (CV, NLP, and deep-learning systems), software, and data systems—from design to delivery. - Stay hands-on , contributing to AI-serving (training and inference), integration, and orchestration components while setting high technical standards. - Partner with Algo teams to connect algorithmic innovation with strong system design, ensuring smooth delivery across Product and Engineering. - Guide architectural evolution , ensuring efficiency, consistency, and maintainability across AI workflows. - Communicate direction and priorities clearly , aligning teams around technical goals. - Mentor engineers and promote technical excellence, sound design, and shared best practices. - Foster collaboration and shared ownership , helping maintain a culture that values clarity, curiosity, and technical excellence. - Uphold quality and reliability—including clean code, testing, and CI/CD discipline—while evaluating and adopting new tools that enhance performance and long-term scalability. Requirements - Master’s or Ph.D. in Computer Science, Engineering, or a related technical field. - 10+ years of software engineering experience with proven architectural depth and a strong hands-on approach. - Proven ability to drive technical direction, make architecture decisions, and align diverse stakeholders. - Strong understanding of AI/ML systems , including deep learning, computer vision, and NLP—able to design and support systems that integrate these models effectively. - Experience designing and building robust software architectures with a focus on scalability, reliability, and maintainability. - Proficiency in Python and modern software engineering practices (clean code, testing, CI/CD, observability, and performance optimization), along with experience using deep learning frameworks such as PyTorch and Tensorflow - Familiarity with cloud environments and MLOps practices , including containerization, orchestration, and model-serving frameworks. - Excellent collaboration and communication skills , with a mindset that combines engineering rigor, curiosity, and purpose. Working at Aidoc Our Perks: - Be part of something big - using cutting-edge technologies to transform the Healthcare industry (while saving patients’ lives) - We work in a hybrid model, with our new offices located at 34 HaMasger Street in Tel Aviv and parking for employees. - Amazing and healthy breakfasts and lunches prepared daily by our personal chef! - Stocked up kitchen & meal card - Wellness: Aidoc employees-only gym, plus Pilates, Yoga, and functional workouts classes. - Amazing culture - collaborative, transparent & fun! - Attractive compensation package & benefits Aidoc is deeply committed to creating an inclusive workplace, and to the principle of equal opportunity for all individuals. We prohibit discrimination and harassment based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by law.
Senior Staff Machine Learning Engineer, LLM/VLM Mo...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware. You will: - Design VLM/LLM model architecture and drive strong alignment between model architectures and hardware architectures. - Optimize model performance for on-device use cases (memory, power, compute constrained environments). - Engage directly with research, software engineering, hardware engineering, and product teams to deliver end-to-end solutions. You have: - 7+ years of experience in Machine Learning, with a focus on large-scale model development (LLM, VLM, or similar foundation models). - Proven expertise in low-latency on-device inference techniques and a deep understanding of hardware acceleration. - Extensive experience with deep learning frameworks (e.g. PyTorch, JAX) and large-scale model training. - A track record of operating effectively under ambiguity, setting direction amid rapidly evolving research and technical constraints - Experience applying large language models or foundation models in complex, safety-critical domains (e.g., autonomy, robotics, or other high-reliability systems) - Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience. We prefer: - Familiarity with large-scale data curation and quality assurance processes for multimodal datasets. - Background in autonomous vehicle perception, motion planning, or decision-making systems. - Publications in top-tier machine learning or computer vision conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV). - PhD in a relevant field. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $298,000 - $368,000 USD
Senior Machine Learning Engineer, Perception LLM/V...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware. You will: - Design, implement, and optimize large-scale continual pre-training pipelines for cutting-edge VLM foundation models. - Conduct research and development on novel pre-training techniques, focusing on efficiently integrating new, diverse, and multimodal data streams (e.g., visual data from different sensors) into existing models. - Develop and rigorously evaluate metrics and methodologies for measuring the performance, and transferability of continually pre-trained foundation models in the context of autonomous driving. - Stay current with the latest advancements in large language models, vision-language models, and continual learning, and translate relevant research into production-ready systems. You have: - 5+ years of experience in Machine Learning, with a focus on large-scale model development (LLM, VLM, or similar foundation models). - Proven expertise in LLM/VLM pre-training, continual learning with large scale datasets. - Strong coding proficiency in Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch). - Hands-on experience with model training, evaluation, and deployment in a production environment. - Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience. We prefer: - Experience in fine-tuning foundation models for autonomous driving or robotics applications - Familiarity with large-scale data curation and quality assurance processes for multimodal datasets. - Background in autonomous vehicle perception, motion planning, or decision-making systems. - Publications in top-tier machine learning or computer vision conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV). - PhD in a relevant field. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $204,000 - $259,000 USD
Machine Learning Engineer Perception LLM/VLM (PhD,...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware. You will: - Design, implement, and optimize large-scale continual pre-training pipelines for cutting-edge VLM foundation models. - Conduct research and development on novel pre-training techniques, focusing on efficiently integrating new, diverse, and multimodal data streams (e.g., visual data from different sensors) into existing models. - Develop and rigorously evaluate metrics and methodologies for measuring the performance, and transferability of continually pre-trained foundation models in the context of autonomous driving. - Stay current with the latest advancements in large language models, vision-language models, and continual learning, and translate relevant research into production-ready systems. You have: - 5+ years of experience in Machine Learning, with a focus on large-scale model development (LLM, VLM, or similar foundation models). - Proven expertise in LLM/VLM pre-training, continual learning with large scale datasets. - Strong coding proficiency in Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch). - Hands-on experience with model training, evaluation, and deployment in a production environment. - Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience. We prefer: - Experience in fine-tuning foundation models for autonomous driving or robotics applications - Familiarity with large-scale data curation and quality assurance processes for multimodal datasets. - Background in autonomous vehicle perception, motion planning, or decision-making systems. - Publications in top-tier machine learning or computer vision conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV). - PhD in a relevant field. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $170,000 - $216,000 USD
Software Engineer-AI Model Bring Up
Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. About Tenstorrent Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. This role sits at the intersection of technical expertise and customer engagement, focused on helping customers and internal teams bring up and optimize AI models on Tenstorrent platforms. This role is hybrid, based out of Tokyo, Japan. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Tenstorrentについて Tenstorrentは、最先端のAI技術で業界をリードし、性能、使いやすさ、コスト効率の常識を変えています。AIによってコンピューティングのあり方が再定義される中、ソフトウェアモデル、コンパイラ、プラットフォーム、ネットワーキング、半導体における技術革新を一体として進化させることが求められています。Tenstorrentの多様な技術者チームは、高性能なRISC-V CPUをゼロから開発してきました。そして、AIへの情熱と、最高のAIプラットフォームをつくりたいという強い思いを共有しています。私たちは、コラボレーション、好奇心、そして難しい課題に真摯に向き合う姿勢を大切にしています。現在、さまざまなレベルの方を歓迎しながらチームを拡大しています。 このポジションは、技術的な専門性と顧客・社内連携の両方が求められる役割であり、顧客や社内チームがTenstorrentプラットフォーム上でAIモデルを立ち上げ・最適化できるよう支援することにフォーカスしています。 本ポジションは日本・東京を拠点としたハイブリッド勤務です。 本ポジションでは、さまざまな経験レベルの候補者を歓迎しています。面接プロセスを通じて適切なレベルを判断し、オファー内容はそのレベルに応じて決定されるため、本求人票上のレベル表記と異なる場合があります. About the Role As a Software Engineer on the AI Models / System Bring-Up team, you will be responsible for porting, validating, and optimizing AI models on Tenstorrent platforms. Working across models, runtime software, and hardware, you will help connect new models, models not yet running on Tenstorrent hardware, and research-stage workloads to stable, high-performance systems. 募集ポジションについて AI Models / System Bring-UpチームのSoftware Engineerとして、Tenstorrentのプラットフォーム上でAIモデルの移植、検証、最適化を担当していただきます。モデル、ランタイムソフトウェア、ハードウェアを横断して、新たなモデルや、まだTenstorrentのハードで動作していないモデル、研究段階のワークロードを安定した高性能システムへとつなげていくポジションです Who You Are - You have hands-on experience with deep learning models in at least one major framework such as PyTorch, TensorFlow, or JAX. - You are strong in Python or C++, with a solid understanding of neural network architectures, training, and inference fundamentals. - You are comfortable developing in Linux environments and can debug issues across software, runtime, and hardware layers. - You are collaborative and curious, and hold a degree in Computer Science, Engineering, Applied Mathematics, or a related field, or have equivalent practical experience. - You have business-level Japanese and sufficient English proficiency to work effectively in a global environment. 求める人物像 - PyTorch、TensorFlow、JAXなど、主要な深層学習フレームワークのいずれかでのモデル実務経験をお持ちの方 - PythonやC++に強みがあり、ニューラルネットワークの構造、学習、推論の基本理解がある方 - Linux環境での開発に慣れており、ソフトウェア・ランタイム・ハードウェアをまたぐ課題のデバッグができる方 - 協調性と好奇心があり、Computer Science、Engineering、Applied Mathematics関連の学位、または同等の実務経験をお持ちの方 - ビジネスレベル日本語と業務遂行が可能なレベルの英語力がある方 What We Need - Port, optimize, and validate AI models such as LLMs, CNNs, recommendation models, and vision models on Tenstorrent hardware. - Port and integrate models into Tenstorrent toolchains and runtime environments. - Design and run experiments to evaluate model accuracy, performance, and stability. - Debug cross-stack issues and collaborate closely with hardware, compiler, and runtime teams. お任せする業務 - LLM、CNN、推薦モデル、ビジョンモデルなどのAIモデルをTenstorrentのハードウェアに移植、最適化したり、検証すること - モデルをTenstorrentのツールチェーンおよびランタイム環境へ移植・統合すること - モデルの精度、性能、安定性を評価するための実験を設計・実施すること - スタック横断の課題をデバッグし、ハードウェア、コンパイラ、ランタイムの各チームと密に連携すること What You Will Learn - How AI models are brought up and optimized on Tenstorrent AI accelerators. - How hardware, compiler, runtime, and model teams work together to build production-ready systems. - Best practices for model porting, optimization, automation, regression testing, and performance tuning. - How to translate real-world model requirements into practical technical solutions. このポジションで得られること - TenstorrentのAIアクセラレータ上でAIモデルをどのように動かし、最適化していくか - ハードウェア、コンパイラ、ランタイム、モデルの各チームがどのように連携して実運用向けシステムを作るか - モデルの移植、最適化、自動化、回帰テスト、性能チューニングのベストプラクティス - 実際のモデル要件を、実用的なソリューションへ落とし込む方法 Nice to Have - Experience with LLM or foundation model inference, including KV-cache optimization and quantization. - Background in compiler or runtime engineering for ML workloads. - Exposure to post-silicon validation, board bring-up, firmware development, or accelerator platforms. - Experience working directly with customers or field teams on AI workload deployment and debugging. - Strong spoken English for technical discussions with global hardware, compiler, runtime, and model teams. 歓迎要件 - LLMまたは基盤モデル推論の経験（KV-cache最適化、量子化を含む） - MLワークロード向けのコンパイラまたはランタイム開発の経験 - ポストシリコン検証、ボードbring-up、ファームウェア開発、アクセラレータ基盤に関する経験 - AIワークロードの導入やデバッグで、顧客やフィールドチームと直接連携した経験 - 本社とのハードウェラ、コンパイラ、ラインタイム、モデルの各チームと連携し、議論できる英会話力 This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
Senior AI Engineer
The Future of Healthcare Needs You At Suki, we’re building technology that listens, understands, and gets out of the way — so clinicians can get back to being clinicians. Our flagship product, Suki Assistant , uses cutting-edge AI to automate clinical documentation in real time, reducing the time it takes to complete notes by 72%. But that’s just the beginning. We’re now powering the next wave of voice-enabled healthcare experiences across telehealth platforms, care delivery tools, and specialty EMRs — through Suki Platform , our proprietary AI infrastructure. Clinicians using Suki spend over 70% less time on admin. Our customers see a 9X ROI in year one. And we’re just getting started. Suki is reimagining the healthcare technology stack to make the lives of clinicians better. Our tech stack includes GCP, Kubernetes, Vertex AI, Golang, Python, C++, PyTorch, JAX, Transformers, GPT, LLAMA, MoE, gRPC, Javascript. What will you do everyday? The Machine Learning team forms the core of Suki's product, driving innovation and predictive excellence. You'll leverage your NLP expertise to enhance our computational infrastructure and improve our technical approaches and algorithms. Working directly on the platform powering our domain services, you'll collaborate with cross-functional teams to address NLP challenges and scale our platform for thousands of doctors. You'll manage and curate large datasets, ensuring data quality and relevance for training and evaluation. You'll monitor and analyze system performance metrics, identifying areas for optimization and implementing necessary improvements. By staying updated on NLP and Generative AI advancements, you'll incorporate innovations to maintain our competitive edge, reducing doctors’ administrative burdens and significantly impacting their daily lives. Requirements:* - 7+ years in overall software experience, with at least 2+ years focused on shipping AI or machine learning systems to production - Demonstrated end-to-end ownership of delivering at least one AI system to enterprise production, spanning the full workflow from problem framing, data pipelines, and model or agent development, through system integration, deployment, evaluation, monitoring, and continuous iteration - Strong working knowledge of LLMs, including prompting, orchestration, RAG, context and cost management - Hands-on experience designing and implementing evaluation frameworks for LLM-based systems, including automated evals, human feedback loops, regression testing, and benchmark design - Experience in building and maintaining distributed backend services for ML/AI applications - Strong grasp of CS fundamentals including abstractions, algorithms, probability, data structures and system design - Preferred - Agentic workflows, SDKs, tool calling; e.g. Google Agent SDK, Langgraph, Langfuse, Langsmith *We don’t necessarily expect to find a candidate that has done everything listed, but you should be able to make a credible case that you’ve done most of it and are ready for the challenge of adding new skills to your resume. Ok, you're sold, but what are we looking for in the perfect candidate? - Expertise: You know how to train models. You understand which models work best, and utilize a balance of LLMs and building custom models to get the optimized solution for accuracy, speed and cost. You watch out for recall, accuracy, and precision. In other words, you’ve done this before. - Action oriented: You love to build. You like to ship fast, quickly iterate, and see amazement in users’ eyes. - Creativity: You enjoy listening to user feedback and then building products in novel ways. You’re resourceful and enjoy finding alternate paths to success. - Problem solving: you use data to help point you in the correct direction. You optimize relentlessly, and understand the impact you’re making on the business. - Humility: You’re humble and love working in a team without ego to deliver products - Adaptability: You thrive in a fast-moving organization that uses light-weight processes and cutting-edge technology to have a huge impact. - Confidence: You trust your abilities and you’re ready to push yourself to the next level. - Communication: You are a clear communicator and demonstrated consensus-builder. What Makes Suki Unique - A mission that matters: Suki is making healthcare technology invisible and assistive—so clinicians can focus fully on patient care. - On a roll: Named by Fast Company as one of the most innovative companies, named Google’s Partner of the Year for AI/ML, named by Forbes as one of the top 50 companies in AI . - An AI product that delivers: Our AI is used by real clinicians at real health systems — and it works. - A team that gets it: We’re former Googlers, Apple engineers, Stanford docs, and healthcare veterans. - Backed by believers: We’ve raised $165M from top VCs like Venrock, First Round, Flare, and March Capital. - On a roll: Named by Fast Company as the Next Big Thing in Tech. Best Leadership Team by Comparably . Technology Innovation Award by Frost & Sullivan. - Massive market: We’re disrupting a $30B+ industry, and our momentum is real. More Than Just a Job You’ll join a team of mission-driven builders who care deeply about what we do and how we do it. We believe in showing up with empathy, iterating constantly, and always putting clinicians first.Suki is an Equal Opportunity Employer. We’re committed to building a team that reflects the diverse communities we serve — and to creating a culture of inclusion, belonging, and bold ambition. The Details In compliance with the State of California Pay Transparency Law, the base salary range for this role is between $225 - $250k in CA. This range is not inclusive of any discretionary bonus or equity package. When determining a candidate’s compensation, we consider a number of factors including skillset, experience, job scope, and current market data.
Staff AI Research Engineer
ABOUT STANDARD BOTS At Standard Bots, we're revolutionizing real-world automation by making robotic systems accessible to everyone. Our AI-powered platform enables robots to tackle unprecedented challenges through an intuitive instruction interface, bringing the power of software automation to physical spaces. ABOUT THE ROLE We're seeking a Staff AI Research Engineer to develop and optimize our AI models and training systems. This is an exciting opportunity to apply your deep ML expertise to cutting-edge AI/robotics applications. You'll work closely with our engineering team to design, implement, and iterate on large-scale AI models while building efficient systems for rapid experimentation and deployment. We have a small team of AI engineers, so we’re looking for someone who is excited to work at a startup, and work across the stack to do what is needed to get a model that achieves a customer problem. We're looking for an experienced engineer ready to make a tangible impact in the AI robotics revolution. This role requires a proven background working on ML planning within the autonomous vehicle space with experience using the latest techniques in diffusion and autoregressive models in a professional setting. If you're excited about building the latest advancements in AI and working in the robotics space, we’d love to hear from you. IN THIS ROLE, YOU WILL: - Design and implement state of the art ML models and training pipelines: - Apply novel machine learning techniques to wide range of robotics applications - Develop efficient data and training strategies - Implement model evaluation frameworks and metrics tracking - Lead model development and iteration with focus on: - Rapid experimentation and prototyping of new model architectures - Performance optimization and model debugging - Transfer learning and fine-tuning strategies - Build robust evaluation and debugging systems to: - Analyze model behavior and failure modes - Implement interpretability tools and visualization frameworks - Track and improve model metrics - Collaborate with engineering team to optimize training infrastructure and deployment YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have 7+ years of AI modeling experience, specifically within the self-driving car industry (or PhD with 3+ years of AI modeling experience in the self-driving car industry) - Proven track record developing and deploying large-scale ML models - Have experience using the latest techniques in diffusion and autoregressive models in a professional setting - Are familiar with training inference and infra pipelines that go from camera input to trajectory output for self driving or robotics - Have experience with RL (reinforcement learning) - Have a strong understanding of modern ML architectures and training techniques - Have experience with model debugging, optimization, and performance tuning - Have a background in implementing ML research papers and adapting academic work TECH STACK - PyTorch (experience with PyTorch required) - Python - NodeJS/Typescript - Docker COMPENSATION AND BENEFITS: The salary range for this role is $250,000 to $300,000, depending on experience. We are open to a variety of seniority levels for this role and will build compensation packages that are commensurate with seniority and skill level. Base salary is just one part of the overall compensation at Standard Bots. All Full-Time Employees are eligible for Employee Stock Options. We also offer a package of benefits including paid time off, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) to regular full-time employees.
Senior AI Research Engineer
ABOUT STANDARD BOTS At Standard Bots, we're revolutionizing real-world automation by making robotic systems accessible to everyone. Our AI-powered platform enables robots to tackle unprecedented challenges through an intuitive instruction interface, bringing the power of software automation to physical spaces. ABOUT THE ROLE We're seeking a Senior AI Research Engineer to develop and optimize our AI models and training systems. This is an exciting opportunity to apply your deep ML expertise to cutting-edge AI/robotics applications. You'll work closely with our engineering team to design, implement, and iterate on large-scale AI models while building efficient systems for rapid experimentation and deployment. We have a small team of AI engineers, so we’re looking for someone who is excited to work at a startup, and work across the stack to do what is needed to get a model that achieves a customer problem. We're looking for an experienced engineer ready to make a tangible impact in the AI robotics revolution. This role requires a proven background working on ML planning within the autonomous vehicle space with experience using the latest techniques in diffusion and autoregressive models in a professional setting. If you're excited about building the latest advancements in AI and working in the robotics space, we’d love to hear from you. IN THIS ROLE, YOU WILL: - Design and implement state of the art ML models and training pipelines: - Apply novel machine learning techniques to wide range of robotics applications - Develop efficient data and training strategies - Implement model evaluation frameworks and metrics tracking - Lead model development and iteration with focus on: - Rapid experimentation and prototyping of new model architectures - Performance optimization and model debugging - Transfer learning and fine-tuning strategies - Build robust evaluation and debugging systems to: - Analyze model behavior and failure modes - Implement interpretability tools and visualization frameworks - Track and improve model metrics - Collaborate with engineering team to optimize training infrastructure and deployment YOU MIGHT THRIVE IN THIS ROLE IF YOU: - Have 5+ years of AI modeling experience, specifically within the self-driving car industry (or PhD with 3+ years of AI modeling experience in the self-driving car industry) - Proven track record developing and deploying large-scale ML models - Have experience using the latest techniques in diffusion and autoregressive models in a professional setting - Are familiar with training inference and infra pipelines that go from camera input to trajectory output for self driving or robotics - Have experience with RL (reinforcement learning) - Have a strong understanding of modern ML architectures and training techniques - Have experience with model debugging, optimization, and performance tuning - Have a background in implementing ML research papers and adapting academic work TECH STACK - PyTorch (experience with PyTorch required) - Python - NodeJS/Typescript - Docker COMPENSATION AND BENEFITS: The salary range for this role is $225,000 to $250,000 depending on experience. We are open to a variety of seniority levels for this role and will build compensation packages that are commensurate with seniority and skill level. Base salary is just one part of the overall compensation at Standard Bots. All Full-Time Employees are eligible for Employee Stock Options. We also offer a package of benefits including paid time off, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) to regular full-time employees.
Staff Machine Learning Engineer
The Opportunity Do you want to lead projects to build and deploy cutting-edge AI technology to help people get unparalleled value from meetings and conversations? Join our core AI team responsible for ML and work alongside industry-veteran scientists and engineers. As a Staff Machine Learning Engineer, you’ll bring your strong software engineering mindset to machine learning in order to scale and optimize our ML systems—creating and transforming innovative research into production-ready features that power Otter’s summarization and conversational intelligence products. Your Impact - Architect, build, and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization, chat, and speech understanding across millions of conversations. - Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for large language and speech models using PyTorch and/or JAX, making principled trade-offs across quality, latency, cost, and reliability. - Design and improve model architectures, loss functions, decoding strategies, and training techniques for speech and language models, informed by both research and production constraints. - Own end-to-end ML system lifecycles , from research prototyping through production deployment, monitoring, iteration, and long-term maintenance. - Partner deeply with product, and infrastructure teams to develop and translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact. - Drive system-level improvements in model performance, robustness, observability, and operational excellence using real-world conversational data at scale. - Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment. - Identify and resolve complex, ambiguous problems in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues. - Mentor and elevate other engineers , influencing team standards, reviewing designs, and contributing to a culture of strong technical decision-making and execution. - Influence applied research and technical roadmaps by identifying promising speech and multimodal modeling approaches, and driving their validation and adoption into production systems We're Looking for Someone Who - Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 10+ years of relevant industry experience; PhD is preferred. - Has deep, hands-on experience building and fine-tuning large language or foundation models, with production experience in ASR, TTS, multimodal, or modern LLM/NLP systems. - Demonstrates strong command of modern ML research, with the ability to critically evaluate new papers and drive innovation by identifying what is production-worthy versus experimental. - Has experience deploying, scaling, monitoring, and operating ML systems in production across training, inference, and serving infrastructure. - Is comfortable working with large-scale speech and conversational datasets, including data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation. - Can lead technical projects independently, driving clarity in ambiguous problem spaces and making sound architectural decisions. - Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration. About Otter.ai We are in the business of shaping the future of work. Our mission is to make conversations more valuable. With over 1B meetings transcribed, Otter.ai is the world’s leading tool for meeting transcription, summarization, and collaboration. Using artificial intelligence, Otter generates real-time automated meeting notes, summaries, and other insights from in-person and virtual meetings - turning meetings into accessible, collaborative, and actionable data that can be shared across teams and organizations. The company is backed by early investors in Google, DeepMind, Zoom, and Tesla. Otter.ai is an equal opportunity employer. We proudly celebrate diversity and are dedicated to inclusivity. *Otter.ai does not accept unsolicited resumes from 3rd party recruitment agencies without a written agreement in place for permanent placements. Any resume or other candidate information submitted outside of established candidate submission guidelines (including through our website or via email to any Otter.ai employee) and without a written agreement otherwise will be deemed to be our sole property, and no fee will be paid should we hire the candidate. Salary range Salary Range: $278,000 to $330,000 USD per year. This salary range represents the low and high end of the estimated salary range for this position. The actual base salary offered for the role is dependent based on several factors. Our base salary is just one component of our comprehensive total rewards package. LI-Hybrid
Senior Machine Learning Engineer
The Opportunity Do you want to lead projects to build and deploy cutting-edge AI technology to help people get unparalleled value from meetings and conversations? Join our core AI team responsible for ML and work alongside industry-veteran scientists and engineers. As a Senior Machine Learning Engineer, you’ll bring your strong software engineering mindset to machine learning in order to scale and optimize our ML systems—creating and transforming innovative research into production-ready features that power Otter’s summarization and conversational intelligence products. Your Impact - Architect, build, and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization, chat, and speech understanding across millions of conversations. - Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for large language and speech models using PyTorch and/or JAX, making principled trade-offs across quality, latency, cost, and reliability. - Design and improve model architectures, loss functions, decoding strategies, and training techniques for speech and language models, informed by both research and production constraints. - Own end-to-end ML system lifecycles , from research prototyping through production deployment, monitoring, iteration, and long-term maintenance. - Partner deeply with product, and infrastructure teams to develop and translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact. - Drive system-level improvements in model performance, robustness, observability, and operational excellence using real-world conversational data at scale. - Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment. - Identify and resolve complex, ambiguous problems in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues. - Mentor and elevate other engineers , influencing team standards, reviewing designs, and contributing to a culture of strong technical decision-making and execution. We're Looking for Someone Who - Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 5+ years of relevant industry experience; PhD is preferred. - Has deep, hands-on experience building and fine-tuning large language or foundation models, with production experience in ASR, TTS, multimodal, or modern LLM/NLP systems. - Demonstrates strong command of modern ML research, with the ability to critically evaluate new papers and drive innovation by identifying what is production-worthy versus experimental. - Has experience deploying, scaling, monitoring, and operating ML systems in production across training, inference, and serving infrastructure. - Is comfortable working with large-scale speech and conversational datasets, including data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation. - Can lead technical projects independently, driving clarity in ambiguous problem spaces and making sound architectural decisions. - Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration. About Otter.ai We are in the business of shaping the future of work. Our mission is to make conversations more valuable. With over 1B meetings transcribed, Otter.ai is the world’s leading tool for meeting transcription, summarization, and collaboration. Using artificial intelligence, Otter generates real-time automated meeting notes, summaries, and other insights from in-person and virtual meetings - turning meetings into accessible, collaborative, and actionable data that can be shared across teams and organizations. The company is backed by early investors in Google, DeepMind, Zoom, and Tesla. Otter.ai is an equal opportunity employer. We proudly celebrate diversity and are dedicated to inclusivity. *Otter.ai does not accept unsolicited resumes from 3rd party recruitment agencies without a written agreement in place for permanent placements. Any resume or other candidate information submitted outside of established candidate submission guidelines (including through our website or via email to any Otter.ai employee) and without a written agreement otherwise will be deemed to be our sole property, and no fee will be paid should we hire the candidate. Salary range Salary Range: $230,000 to $265,000 USD per year. This salary range represents the low and high end of the estimated salary range for this position. The actual base salary offered for the role is dependent based on several factors. Our base salary is just one component of our comprehensive total rewards package. LI-Hybrid
Software Engineer, CUDA Deep Learning Systems
We are looking for an experienced and highly motivated software professional to work on pioneering initiatives and projects at the intersection of CUDA and Deep Learning Systems. As the complexity and scale of artificial intelligence continue to grow, the intersection of advanced deep learning architectures, massive-scale distributed computing, and low-level hardware optimization has never been more critical. Our team is dedicated to exploring and prototyping next-generation ideas that bridge the gap between deep learning algorithms and CUDA, pushing the boundaries of what is possible on modern accelerator architectures. Join our dynamic, research-oriented team to help unlock maximum hardware performance for emerging AI workloads. You will be a crucial member of a highly technical group exploring uncharted territories in model optimization, custom kernel development, and cluster-scale AI systems design. If you are passionate about the fundamentals of deep learning and thrive on squeezing every ounce of performance out of advanced computing systems from a single GPU to supercomputer clusters, we want you on our team! What you will be doing: Explore, research, and prototype novel systems optimizations for advanced deep learning models at the intersection of high-level DL frameworks and low-level CUDA through modeling, simulation, and silicon prototyping. Architect and optimize distributed computing systems that scale seamlessly from a single node to massive, cluster-scale supercomputing environments. Design, implement, and optimize custom high-performance CUDA kernels tailored to emerging neural network architectures and workloads. Analyze complex hardware-software interactions to identify and resolve performance bottlenecks in both training and inference pipelines. Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and algorithms that improve accelerator compute utilization, memory bandwidth, cross-node network communication efficiency and programmability. Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning. Write clean, effective, and maintainable code, ensuring exploratory prototypes can smoothly transition into open-source releases, upstream framework integrations, internal tools, or closed-source commercial products. What we need to see: BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience). 2+ years of relevant industry experience or equivalent academic experience after degree achievement. Strong proficiency in C++ and Python programming. Solid background in the fundamentals of Deep Learning with a focus on transformers. Strong understanding of distributed computing principles, multi-node scaling, and the unique performance challenges of cluster-scale execution. Proven experience in systems programming, computer architecture, and low-level systems performance optimization. Familiarity with deep learning accelerator architectures such as the GPU and hands-on experience with CUDA programming, kernel optimization, and workload profiling Experience profiling and optimizing generative AI models, including but not limited to, pioneering large language models. Research background in machine learning systems or adjacent fields and experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models. A track-record of initiative and willingness to deep-dive on problems across the stack. Ways to stand out from the crowd: Deep expertise in performance internals and execution graphs of major deep learning training and inference frameworks (e.g., PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, Megatron). Hands-on experience with communication libraries (e.g., NCCL, MPI, UCX) and distributed machine learning techniques (e.g., pipeline, tensor, expert parallelism). Knowledge of numerical methods and low-precision arithmetic (e.g., NVFP4, MXFP4, FP8, INT8) and their impact on deep learning accuracy and performance. Background in deep learning compilers and ML systems, including graph-level and codegen tools (e.g., Triton, XLA, torch.compile) and highly parallel/RL-style simulation environments. Experience designing and implementing agentic AI systems applied to complex systems and infrastructure problems. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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