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Computer Vision Engineer, Space
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 Anduril's Space team is dedicated to expanding our AI-powered capabilities into the final frontier, enhancing Space Domain Awareness, Space Control, and Command and Control for U.S. military and allied partners. We're developing fully integrated hardware and software systems, including Lattice for Space Missions and modular payloads, to address growing threats in space and ensure our Guardians maintain a decisive advantage in this contested warfighting domain. ABOUT THE JOB We are looking for a Computer Vision Engineer to join our rapidly growing team in Costa Mesa, CA . In this role, you will be responsible for working on and understand the design of all perception subsystems to include but not limited to hardware sensors and advanced processing platforms, navigation algorithms, flight software implementation, subsystem integration & test (I&T), and vehicle I&T. The computer vision engineering team will work closely with related teams, including Sensors, GNC, Avionics, Systems, Flight Software, Mission Operations, and Ground Software. The Computer Vision Engineer will work algorithm design, truth and physics modeling, scene rendering, simulation and analysis for a wide variety of spacecraft and space missions to include but not limited to LEO, MEO, GEO, Reentry, and RPOD (Rendezvous Proximity Operations and Docking). The CV Engineer will help lead successful implementation, validation and CV operations of Anduril’s fleet of space vehicles. This role is directly tied to ongoing, funded programs within Anduril’s Space Business Line. The programs require building and fielding a resilient, software-defined spacecraft systems across numerous mission threads. We work with mission partners and customers to deploy reliable and robust capabilities on operationally-relevant fielding timelines to meet complex challenges across the DOD and IC . WHAT YOU’LL DO - Propose and prototype innovative solutions to solve real world problems, leveraging the latest state-of-the-art techniques in the field - Develop and maintain core libraries and runtime applications - Integrate classical and geometric methods in computer vision with ML methods - Work space vehicle CV software and hardware subsystems for various spacecraft efforts in all orbital regimes and work closely with partners for successful implementation - Develop modern, software-defined approaches to autonomous spacecraft operations with maneuvering capabilities to successfully accomplish mission objectives - Develop appropriate test plans and procedures to validate the CV system during ground checkout, on-orbit commissioning and operations - Collaborate across multiple teams to plan, build, and test complex functionality - Coordinate with end-users, other operators and customers to turn needs into features while balancing user experience with engineering constraints - Support challenging schedules during ground testing, launch windows and on-orbit operations of the spacecraft systems - Design of flight software and firmware, algorithms, and simulation products - Development of space vehicle autonomy tools for dynamic space operations - Test process development and execution - Define automated fault detection and responses - Provide hardware-in-the-loop and monte-carlo simulation capabilities REQUIRED QUALIFICATIONS - MS or PhD in Machine Learning, Robotics or Computer Science, Image Science with emphasis on Computer Vision - BS in Computer Science, Machine Learning, Electrical Engineering, or related field - Advanced professional experience developing and benchmarking ML algorithms on large-scale datasets - High proficiency in C++ development in a Linux environment - Experience in one or more of the following: Objection Detection, Object Tracking, Instance Segmentation, Semantic Segmentation, Semantic Change Detection, Natural Feature Tracking (NFT) - Experience in one or more of the following: Visual Odometry, SLAM, Multi-view Geometry, Structure from Motion, 3D Geometry, Discriminative Correlation Filters, Stereo, Neural 3D Reconstruction, Multi-band sensor processing, RGB-D and LIDAR Sensor Fusion - Ability to quickly understand and navigate complex systems and detailed requirements - Familiarity with terminal guidance, rendezvous proximity operations and docking, orbital mechanics with propulsive spacecraft, and/or spacecraft/missile GNC - Clear communication and organizational skills including documentation and training material - Currently possesses and is able to maintain an active U.S. Top Secret security clearance PREFERRED QUALIFICATIONS - Experience with Matlab, Simulink, Python, Go, C++ and/or Linux systems - A desire to work on critical software and hardware designs in the space domain - Strong preference for candidates possessing computer vision, vision navigation, image processing, feature tracking, SLAM, Open CV, and NFT - Experience testing CV subsystems in laboratory environments that mimic the space environmental constraints - Experience with orbital mechanics and resident space object tracking capabilities - Experience conducting spacecraft operations and satellite command and control with an emphasis on system reliability and uptime - Experience with testing/validation leveraging FlatSats, Hardware-in-the-Loop testbeds and digital spacecraft simulators through nominal and fault scenarios - Experience with computer vision and perception algorithms to support GNC operations - Experience developing 3-DOF simplified and 6-DOF high-fidelity dynamics simulation models used for GNC systems analysis and validation - Exposure to US satellite operations policy and constraints for relevant mission threads in all orbits 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.
Robotics Software Engineer
Who We Are Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future. Achieving real-world performance requires extremely tight system latency, reliable sensor pipelines, and end-to-end engineering that makes perception and control loops work at real-time speeds. As a Runtime Software Engineer, you’ll engineer the low-latency, high-throughput systems that underpin our physical intelligence model. You won’t be designing ML models - you’ll be the person who makes them run flawlessly in production, optimizing every layer from OS to camera pipeline to networking. You’ll collaborate closely with researchers, platform engineers, and robotics operators to identify bottlenecks and extract maximum performance from the entire system. The Team The Runtime team is responsible for building the core platform that Pi’s robots, sensors, and evaluation pipelines rely on. The team spans Linux systems engineering, camera and sensor pipelines, robot actuator controllers, networking, real-time IO, and performance tooling. They ensure our ML models and control systems operate under strict latency budgets and are robust under real-world conditions. In This Role You Will -Own Real-Time Pipelines: Engineer low-latency, high-reliability sensor and actuator pipelines across Linux, drivers, and middleware. -Optimize System Performance: Profile and optimize across compute, I/O, memory, scheduling, networking, and storage to meet real-time constraints and increase throughput. -Build OS-Level Capabilities: Extend or modify Linux components, drivers, and scheduling to achieve deterministic behavior under load. -Streaming & Video Systems: Develop and optimize real-time video streaming systems where frame timing and packet scheduling matter. -Reliability & Debugging: Build tooling for profiling, tracing, and debugging timing issues across distributed systems and hardware interfaces. -Cross-Functional Collaboration: Work with researchers, hardware engineers, and operations teams to integrate optimized pipelines into production workflows. What We Hope You’ll Bring -Strong programming skills in C++, Rust, or Python, with experience building and optimizing production software. -Experience with Linux systems programming (syscalls, drivers, kernel parameters, scheduling, memory/IO subsystems). -Background in real-time or near–real-time systems, VR/AR, video pipelines, 3D engines, or streaming systems where latency budgets are strict. -Ability to optimize across the entire stack - kernel scheduling, drivers, networking, GPU/CPU workloads, video frameworks, and distributed components. -Experience with profiling tools (perf, tracing, eBPF, GPU profilers, network analyzers) and comfort diving into complex performance issues. -A mindset oriented around determinism, throughput, frame budgets, jitter minimization, and real-time correctness. -Ability to collaborate deeply with researchers and platform engineers to translate high-level model requirements into real-world system performance. Bonus Points If You Have -Experience with VR/AR platforms or low-latency 3D engines. -Camera system expertise (synchronization, capture pipelines, codecs, GPU offload). -Streaming/video conferencing stack experience (WebRTC, real-time transport optimizations). -Background in robotics, autonomous systems, SLAM pipelines, or perception systems (implementation, not research). -Expertise in kernel-level engineering, device drivers, or high-performance networking. -Familiarity with distributed systems that process real-time data flows. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Rendering Systems Engineer - Simulation & Syntheti...
ABOUT WORLD LABS World Labs https://www.worldlabs.ai/ is a frontier AI research and product company advancing spatial intelligence, the next frontier beyond large language models. Co-founded by Dr. Fei-Fei Li https://profiles.stanford.edu/fei-fei-li, Justin Johnson https://www.linkedin.com/in/justin-johnson-41b43664/ and Ben Mildenhall https://www.linkedin.com/in/ben-mildenhall-86b4739b/, the company is pioneering world models that perceive, generate, reason, and interact with virtual and physical worlds. The company’s flagship product, Marble https://marble.worldlabs.ai/, transforms text, images, and video into fully navigable 3D worlds, unlocking applications across gaming, film, architecture, robotics, and immersive digital experiences. Backed by leading investors and with over $1B raised, World Labs is assembling a world-class team at the intersection of AI research and real-world deployment. ROLE OVERVIEW We are seeking a Senior/Staff Rendering Systems Engineer to build and scale high-throughput, Unreal Engine–based rendering systems for synthetic data generation. You will own the low-level rendering and GPU performance work required to run many-world rendering efficiently across cloud GPU fleets. You will profile end-to-end workloads, diagnose performance/IO bottlenecks, and implement production-quality C++ and CUDA systems within and around Unreal Engine. You will also partner with infrastructure and data teams to scale distributed execution and integrate the renderer with internal simulation and data pipelines. WHAT YOU WILL DO - Deep experience modifying Unreal Engine’s renderer to support run independent worlds or cameras across GPUs and build the cloud job-orchestration layer - Maximize hardware throughput and GPU efficiency via custom parallelization, caching strategies, and high-performance interconnect communication. - Integrate rendering system seamlessly with proprietary internal toolchains and data pipelines. - Perform rigorous profiling, roofline analysis, and root-cause diagnosis to resolve compute and I/O limits. - Collaborate with research teams to accelerate experimental iteration and enhance system robustness at scale. KEY QUALIFICATIONS Strong candidates must demonstrate mastery in low-level Unreal rendering architecture and GPU programming. While broad experience on distributed multi-GPU compute are highly valued, core graphics performance engineering remains the primary prerequisite. - Strong background in performance engineering, including telemetry profiling, roofline analysis, latency/throughput optimization, and systematic root-cause analysis. - Deep understanding of Unreal rendering stack, familiar with its codebase at platform-agnostic rendering layer, the command lists abstract layer, and the RHI abstraction layer, alongside practical experience customizing engine source at various layers and shader pipelines. Familiar with its GPU resource lifetime and synchronization, as well as async compute. - Working knowledge of Nanite, Lumen, and associated graphics debugging suites. - Advanced low-level GPU optimization skills (CUDA/Vulkan), with emphasis on kernel-level tuning, memory hierarchy management, and memory bandwidth efficiency. - High proficiency in C++, CUDA, Python (and possibly Rust), accompanied by practical Vulkan implementation experience. - Proficiency in rendering material adjustment and custom shader development pipelines. PREFERRED QUALIFICATIONS - 3-4+ years of engineering experience within AI research labs, machine learning enterprises, or robotics and autonomous vehicle organizations. - Familiarity with distributed data interconnects and collective communications primitives (e.g., NVLink, NCCL); A strong plus, but not a substitute for the core skills above. - Proven track record deploying or serving large-scale generative, diffusion, spatial, or video foundation models. - Hands-on creation of performance-profiling, telemetry, and observability toolchains for real-time graphics pipelines and GPU workloads. WHO YOU ARE - Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible. - Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger. - Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people. - Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence. We're hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you're ready to work on technology that will reshape how machines perceive and interact with the world - then World Labs is your launchpad. Join us, and let's make history together. Logistics: Location: This role is based in San Francisco, California; we work in-person in the office 5 days a week. Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. Benefits: We offer generous health, dental, vision and 401K benefits, flexible PTO, paid parental leave, and relocation support as needed. Equal Employment Opportunity World Labs is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected under applicable law. We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.
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.
MTS - Agent Research
About Etched Etched is building hardware for frontier intelligence. We co-design chips, racks, software, and manufacturing to deliver best-in-class throughput and latency across both prefill and decode workloads. Our first products are heavily focused on inference. Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history. Job Summary As a research lead, you will set the research direction for this effort and spearhead the development of self-improving agents that reliably complete difficult, long-running engineering tasks. At Etched, this loop compounds: better agents help us build and optimize our systems faster, better systems expand our capacity to run more experiments, generating feedback and training data that further improve agent capabilities. Your job is to make this loop work in practice. This requires strong research judgment. Where will better memory, context, or tooling unlock new capabilities? Which tasks benefit from structured workflows, and which require more open-ended autonomy? When do agent limitations call for changes to the harness, and when do they require custom models trained with supervised fine-tuning or reinforcement learning? You will set research priorities and design experiments that guide our investments in in-context learning, model training, and harness design. You will work closely with engineers embedded across the company, with an initial focus on ASIC development, inference, and supercomputing. First, you will build the environments that let existing models do their best work through better tools, context, feedback, and in-context learning. Then, you will use the resulting trajectories and evaluations to develop specialized models where they can improve capability, reliability, or cost. This is a hands-on research leadership role. You should be comfortable setting direction, writing and debugging code, and carrying an idea through to production. Research success means measurable improvements in the work Etched can accomplish, with disciplined use of compute. Key Responsibilities - Own the research agenda for agentic systems across Etched. Identify the most important bottlenecks, prioritize promising approaches, and decide when to scale. - Work directly with domain engineers to understand real workflows, diagnose where agents fail, and turn those failures into research questions with concrete evaluations. - Develop systems for long-running tasks, including memory, context management, tool use, planning, and coordination between agents. Determine which approaches generalize across teams and which require domain-specific methods. - Build evaluations that measure correctness, reliability, and efficiency on real engineering tasks, and verify that gains transfer to production. - Train custom models using SFT, RL, and distillation, with the agent harness in the training loop. Use execution trajectories and failure analysis to guide training data and reward design. - Scale experiments to hundreds or thousands of concurrent agents while maintaining reproducibility, observability, and control over compute costs. You may be a good fit if you have (Must-have qualifications) - Strong research taste and a record of turning ambiguous problems into clear hypotheses, decisive experiments, and useful systems. - Excellent engineering ability. You can move between research exploration, agent experimentation, low-level debugging, and production execution, and you take responsibility for systems working reliably. - Experience building agents for complex, multi-step tasks, with a practical understanding of context, memory, tools, evaluation, and failure recovery. - Hands-on experience with LLM post-training, including SFT and RL. - A track record of making meaningful progress under compute and data constraints. You care about the quality of the experiment and the cost of a successful outcome. - High agency and comfort with unfamiliar domains. You can find the problem, acquire the missing context, and drive the work forward. - The ability to set technical direction and work closely with domain experts to turn research into systems they trust and use. Strong candidates may also have experience with (Nice-to-have qualifications) - ASIC design or verification, accelerator kernels, inference systems, or large-scale compute infrastructure. Benefits - Medical, dental, and vision packages with generous premium coverage - $500 per month credit for waiving medical benefits - Housing subsidy of $2,500 per month for those living within walking distance of the office - Relocation support for those moving to San Jose (Santana Row) - Various wellness benefits covering fitness, mental health, and more - Daily lunch and dinner in our office - Unlimited compute budget subject to ROI justification Base Compensation Range - $175K – $275K - Plus Significant Equity How we’re different Etched believes in the Bitter Lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html. We are the first inference-focused frontier AI system, betting early on transformer and transformer-like architectures and on increasing model sizes. Our addressable market is the entirety of inference, unlike many of our competitors. We are a fully in-person team in San Jose (Santana Row), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both and work across disciplines as needed.
Research Scientist, Simulation
Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors. THE ROLE As a Research Scientist focused on Simulation, you will build the simulated worlds that power Dyna's loco-manipulation policies. Our robot is a wheeled mobile manipulator. Mobility and manipulation are tightly coupled, not two separate problems, so simulating one without the other misses what actually matters for our policies. You'll own the pipeline end-to-end: from real-to-sim reconstruction of the facility-scale environments our robots navigate, through procedural scene generation, to sim-based training data and evaluation for policies that jointly control base motion and arm manipulation. WHAT YOU'LL DO Real-to-Sim Reconstruction: Build a pipeline that reconstructs real, facility-scale environments into simulation (3D reconstruction, photorealistic re-rendering). That means full rooms and aisles our wheeled base navigates, not just tabletop scenes. Facility-Scale Scene Generation: Procedurally generate navigable environments (layouts, obstacles, object placement) that stress-test loco-manipulation policies across the range of spaces our robots actually operate in. Loco-Manipulation Sim-for-Data-Gen: Generate synthetic training data for policies that jointly reason about base positioning and arm manipulation (approach angles, reachability, obstacle-aware repositioning), and feed real-world failure modes back into new sim scenarios. Loco-Manipulation Evaluation: Build simulation benchmarks that test the joint base+arm policy (VLA / imitation learning / diffusion models) as a whole, not manipulation in isolation on a fixed base. Rendering for ML, Not Just Physics: Push photorealistic rendering quality specifically to close the sim-to-real gap for vision-based loco-manipulation. Rendering fidelity is a first-class deliverable, not an afterthought on top of physics accuracy. Cross-Team Collaboration: Partner with the AI Research team on where simulated data and evaluation most accelerate loco-manipulation policy development, and with Data Ops on what's worth capturing in the real world vs. generating in sim. WHAT YOU'LL BRING - MS or PhD in CS/Robotics/Graphics, or equivalent hands-on experience. We care more about demonstrated depth building simulation/rendering systems than a fixed years-of-experience bar. - Hands-on experience with simulation stacks (MuJoCo, Isaac Sim/Isaac Lab, SAPIEN, Omniverse, Blender, or similar) for robotics or graphics. - Experience with procedural scene/asset generation, domain randomization, or photorealistic rendering pipelines at scale (not academic-scale one-off scenes). - Familiarity with training/evaluating manipulation or loco-manipulation policies (imitation learning, VLA, diffusion, or RL) and how simulated data actually feeds into them. - Experience simulating mobile bases (wheeled, tracked, or holonomic) is a real plus. Most of the field's simulation talent comes from fixed-arm tabletop manipulation or legged-humanoid balance/gait work, and neither maps cleanly onto a wheeled mobile manipulator. - Prior research or engineering background in loco-manipulation or whole-body control itself, coordinating mobile-base motion with arm manipulation, independent of any simulation-specific experience. - Strong Python skills; comfort with PyTorch or JAX for anything touching model training/eval. - Senior enough to define your own research agenda and exercise independent judgment on where simulation adds the most leverage. This is closer to a founding-type ownership role than a narrow IC slot on an existing pipeline. BONUS POINTS FOR - Experience with real-to-sim-to-real pipelines (3D reconstruction, NeRF/Gaussian splatting, differentiable rendering). - Exposure to world-model / video-prediction research (e.g. learned dynamics or latent world models) as a complement to classical physics simulation. - GPU-scale physics simulation experience (CUDA, large-batch parallel sim) rather than single-instance sim. - Background simulating wheeled/mobile manipulators specifically (e.g. Boston Dynamics Stretch, warehouse/logistics robotics) rather than legged humanoids or fixed-base arms. - Publications at CoRL, RSS, ICRA, NeurIPS, CVPR, or SIGGRAPH. - Experience leading or mentoring within a small research team. At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect. Don’t let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you’re passionate about robotics, no matter your discipline, we want to hear from you, even if you don't check every box.
Join Our Talent Network - Senior/Staff Machine Lea...
About Dexterity At Dexterity, we believe robots can positively transform the world. Our breakthrough technology frees people to do the creative, inspiring, problem-solving jobs that humans do best by enabling robots to handle repetitive and physically difficult work. We’re starting with warehouse automation, where the need for smarter, more resilient supply chains impacts millions of lives and businesses worldwide. Dexterity's full-stack robotics systems pick, move, pack, and collaborate with human-like skill, awareness, and learning capabilities. Our systems are software-driven and hardware-agnostic and have already picked 100+ million goods in production. And did we mention we’re customer-obsessed? Every decision, large and small, is driven by one question – how can we empower our customers with robots to do more than they thought was possible? Dexterity is one of the fastest-growing companies in robotics, backed by world-class investors such as Kleiner Perkins, Lightspeed Venture Partners, and Obvious Ventures. We’re a diverse and multidisciplinary team with a culture built on passion, trust, and dedication. Come join Dexterity and help make intelligent robots a reality! About the Role As a Senior/Staff Machine Learning Engineer, you will be working on a myriad of challenges related to robot task and action planning. You will leverage techniques from machine learning to solve hard sequential decision problems that require reasoning about the physical world and its dynamics. You will also stay abreast of the latest progress in imitation learning, reinforcement learning, and other related fields in order to further develop Dexterity’s technology foundations in Physical AI. Additionally, you will be responsible for updating and scaling our current ML pipelines to cover more scenarios and improve accuracy. Dexterity's robotic solutions integrate data from a multitude of sensors, including RGB cameras, depth sensors, force-torque sensors, encoders, system telemetry and human input. To better inform the planning algorithms, you may work on sensor fusion and state estimation techniques to leverage this multimodal sensory data. You will also work closely with the data platform, physics simulation, and robot operations teams to develop effective and efficient ways to improve the system’s internal world model. Dexterity has an expanding set of algorithmic challenges as we deploy new robotic applications, including areas such as: - Improving packing algorithms to build taller, denser, more stable structures with a wider variety of objects. - Solving the logistics task of moving and sorting inventory throughout a warehouse. - Building models that understand physics and geometry for both short- and long-horizon tasks. In addition to curating datasets and developing/improving machine learning models, you will be responsible for building data flywheels. Ideally, you will bring data-driven productization experience and help the team broadly in qualifying, deploying and updating models. Responsibilities Design and implement machine learning solutions across Dexterity’s robotics stack, including but not limited to perception, decision-making, action scoring, and predictive modeling Own the full ML development cycle for these solutions: data curation, labeling, training, evaluation, deployment, and iteration Build performant training and inference pipelines using PyTorch, with production-readiness and scalability in mind Collaborate closely with robotics, data platform, and simulation teams to integrate ML into real-time, latency-sensitive robotic systems Use profiling, monitoring, and experiments to optimize model performance and reliability Ensure reproducibility, traceability, and modularity across training and serving pipelines Maintain clean, production-quality code in Python (and C++ where required) Help establish best practices for model versioning, dataset management, and ML operations Required Skills Degree in Computer Science, Electrical Engineering, or Mathematics 5+ years of industry experience applying machine learning to real-world, production systemsStrong Python skills and deep experience with PyTorch Ability to work fluently across ML tasks, e.g., classification, regression, ranking, segmentation, and structured prediction Strong engineering background with experience profiling, debugging, and optimizing model and pipeline performance Proven ability to design and maintain reliable systems, from model training to field deployment Experience with cloud-based infrastructure (AWS, GCP, Azure) and containerized environments (Docker) Familiarity with Linux, Git, CI/CD) and software development best practices (unit/acceptance/integration testing, code reviews) Nice to haves Prior experience in robotics, autonomous systems, or real-time ML applications Exposure to multimodal data (e.g., RGBD, force-torque, pose estimates, telemetry) Experience deploying models using serving stacks like NVIDIA Triton, TorchServe, or custom low-latency frameworks Background in computer vision, geometric learning, or time-series modeling Experience with Kubernetes, Ray or other distributed training and inference systems Previous startup experience or experience in fast-paced, cross-disciplinary environments
Senior Technical Program Manager (Engineering) - A...
COMPANY OVERVIEW Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram. COMPANY OPERATING RHYTHM At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance. Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do. Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5. Deepgram is seeking a Senior Technical Program Manager (AI Tooling & Systems) for Engineering to drive execution of large-scale ML infrastructure and AI tooling initiatives. In this role, you'll own the end-to-end delivery of programs that span model serving infrastructure, ML pipelines, internal AI tooling, and real-time inference systems—working closely with our ML engineers, research teams, and product to unlock capability at scale. You'll thrive here if you enjoy creating clarity around complex ML system tradeoffs, building tools and processes that accelerate model development and deployment, and partnering across research, engineering, and product to align on technical strategy and execution. WHAT YOU'LL DO - Own end-to-end delivery of AI infrastructure programs—from model training pipelines and experiment tracking to inference serving and production monitoring - Define technical architecture, integration patterns, and rollout strategies for new ML systems and tooling (e.g., vector databases, model servers, evaluation frameworks, prompt engineering platforms) - Serve as connective tissue between ML research, ML engineering, product, and data teams to align on ML system requirements, capability roadmaps, and deployment timelines - Drive cost and latency optimization for real-time inference workloads at scale - Build lightweight internal tools and processes to accelerate ML iteration cycles (experiment tracking, model versioning, A/B testing infrastructure) - Identify and resolve technical bottlenecks in training pipelines, serving infrastructure, and model evaluation workflows - Work closely with ML practitioners to translate research breakthroughs into scalable, observable systems YOU'LL LOVE THIS ROLE IF YOU - Are passionate about building ML systems and infrastructure that powers frontier AI applications - Enjoy optimizing inference cost, latency, and throughput for LLM and multimodal workloads at scale - Love solving hard problems at the intersection of ML research and production systems (e.g., distillation, quantization, batching strategies) - Are excited about frontier model serving technologies, vector search, and real-time ML inference - Want to directly enable ML researchers and engineers to iterate faster and ship better models IT'S IMPORTANT THAT YOU HAVE - 5+ years of program management or technical leadership in ML infrastructure, ML platforms, or AI tooling (or equivalent) - Strong technical acumen in ML systems—ideally hands-on experience as an ML engineer, systems engineer, or ML infrastructure engineer - Experience coordinating cross-functional ML programs (e.g., model training → evaluation → serving → monitoring) - Proven ability to translate ML/research requirements into robust, scalable infrastructure - Comfortable working in ambiguity and helping teams navigate complex technical tradeoffs (e.g., accuracy vs. latency vs. cost) - Excellent communication with both technical and non-technical stakeholders - Familiarity with high-growth or startup environments IT WOULD BE GREAT IF YOU HAD - Hands-on experience with model serving frameworks (vLLM, TensorRT, TorchServe, or similar) - Experience optimizing LLM or speech/audio model inference (quantization, distillation, KV-cache optimization, batching strategies) - Familiarity with ML experiment tracking and versioning tools (MLflow, Weights & Biases, DVC, or similar) - Background in feature stores, vector databases, or real-time ML systems - Knowledge of cost optimization for GPU/ML workloads on cloud and on-premise infrastructure - Experience with multi-region model serving or edge deployment - Hands-on with relevant frameworks (PyTorch, CUDA, Hugging Face, etc.) or cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML) Notice: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com http://deepgram.com email address. If you've received a message claiming to be Deepgram, please forward it to careers@deepgram.com.
Sr. Developer Advocate, AI and Machine Learning
RDQ427R542 At Databricks, we are passionate about enabling data teams to solve the world's most challenging problems — from making the next mode of transportation a reality to accelerating medical breakthroughs. We do this by building and running the world's best data and AI platform, enabling our customers to use deep data insights to improve their businesses. Our ability to execute this mission depends on building trust and recognition within an ever-growing community of data engineers, analysts, scientists, machine learning practitioners, and AI practitioners. Are you the person who is already at every AI meetup in the Bay Area — not to be seen, but because that's where the interesting conversations are happening? Are you a hands-on practitioner in generative AI, agents, and MLOps who can hold your own in a room full of AI engineers and leave them with something they can actually use? As a Senior Developer Advocate for AI and Machine Learning, you'll be the voice of Databricks in the Bay Area AI community and a crucial link between our engineering teams and the global community of data scientists and AI engineers. You'll focus on how practitioners build, evaluate, govern, and ship agentic systems on the Databricks Data + AI Platform — with particular depth in Genie Agents, Genie Code, Genie One, MLflow, and Databricks Unity AI Gateway. You'll translate what you learn from customers and the community into reference implementations, demos, and content that are production-ready rather than merely impressive on stage. This is a role for someone who wants to be in the room. San Francisco and the wider Bay Area are the densest concentration of AI practitioners in the world, and we want Databricks to be present and useful in that community — at meetups, hack nights, demo nights, founder dinners, and the hallway conversations around them. You'll build genuine relationships with the people building the future of AI here, and you'll bring what you hear back to our product and engineering teams. Your responsibilities will encompass a wide range of knowledge-sharing activities. You'll deliver engaging talks, host panels and meetups, create blogs and video content, develop courseware, answer questions in community forums, and meet one-on-one with influential AI engineers and data scientists. Reporting to the Head of Developer Relations, you'll collaborate with fellow Developer Advocates and program managers to create and execute a cohesive and impactful developer relations strategy. The ideal candidate embodies the values of our Developer Relations team: a deep passion for data and AI, genuine empathy for developers' needs, and a strong commitment to explaining our products clearly and honestly. More about the DevRel team The Developer Relations (DevRel) team at Databricks is dedicated to building and fostering relationships with communities of data practitioners. Our goal is to drive awareness and adoption of the Databricks Data + AI Platform — including Lakeflow, Lakebase, Genie Agents, and AI/BI — alongside the open source projects that underpin it, such as Apache Spark™, Delta Lake, Unity Catalog, MLflow, Apache Iceberg™, and Omnigent. Our team also owns programs like the Databricks MVP program, the user group program, and DevConnect roadshows. The impact you will have - Be a consistent, recognized presence in the Bay Area AI community — speaking at local user groups and meetups, showing up to the events that matter, and building real relationships with the practitioners and founders shaping the space. - Own medium-to-large advocacy projects end-to-end, from scoping through delivery, for the AI and agents area. - Create production-ready reference implementations, starter kits, and demos that show practitioners how to build, evaluate, and govern agents on Databricks — and get them to first value in minutes, not hours. - Create high-quality educational content — videos, sample notebooks, datasets, tutorials, courseware, and blog posts — and reuse each core asset across formats to reach practitioners wherever they already are. - Drive awareness and adoption of Databricks AI capabilities through speaking engagements at industry events and conferences. - Act as a trusted advisor between the AI community and our product teams, and influence roadmap discussions with what you learn. - Expand and nurture the Databricks AI and ML community by growing meetups and user groups and supporting practitioners in online communities. - Gather and analyze community feedback to drive continuous improvement of Databricks products and services. - Mentor and unblock less-experienced advocates in content, demos, and public speaking. What we look for - 5+ years of combined experience as a developer advocate and in a hands-on technical role — software engineer, ML engineer, data scientist, or solutions architect. - Professional experience actually building and operating AI or ML systems, not only presenting about them. - Subject matter knowledge of solving AI and ML problems at scale, including agent design, evaluation, and production operations. - Comprehensive understanding of the Databricks AI ecosystem, including Genie Agents, Genie, MLflow, and Databricks Unity AI Gateway. - Strong Python skills and fluency with the current agent and ML tooling landscape. - Based in the Bay Area and genuinely willing to be out in the community on a regular basis, including evenings. - Active participation and recognized leadership in AI and ML community forums, chats, and meetups — measured by what the community builds with your help, not by follower count. - A public portfolio of technical work: tutorials, talks, repositories, or sustained community contributions. - Proven track record in nurturing developer communities, organizing user groups, and facilitating meetups. - Exceptional communication skills, with a talent for articulating complex concepts through writing, teaching, video, and public speaking. - Deep empathy for developer needs, with the ability to craft engaging experiences across tutorials, notebooks, and community platforms. - Adept at collaborating with cross-functional stakeholders to align community initiatives with product objectives. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Zone 1 Pay Range $149,200 - $205,150 USD About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Software Engineer, Inference AI/ML
CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com . What You’ll Do: Join the Inference team to ship production features that improve latency, reliability, and cost for model serving on our GPU platform. As an IC1, you’ll implement well-scoped changes, learn our operational practices, and grow quickly with mentorship from experienced engineers. About the role: - Implement well-scoped features and fixes in Python/Go/C++ for model-serving services (e.g., Triton, vLLM, TensorRT-LLM, Ray Serve). - Write tests, code comments, and short design docs; participate in code reviews. - Add basic metrics and dashboards; assist with alarms and runbooks. - Follow on-call runbooks and learn incident response in a guided rotation. - Contribute to performance experiments (e.g., request batching, concurrency, caching) with guidance. Who You Are: - BS/MS in CS, EE, or related field, or equivalent practical experience. - Foundations in data structures, algorithms, and networked services. Experience with Python or Go (C++ a plus) and Linux fundamentals; Git/CI basics. Exposure to containers and Kubernetes (coursework or projects welcome). Curiosity about GPU inference concepts (micro-batching, KV cache, streaming). Preferred: - Internship or project that deployed a microservice or ML inference demo. - Coursework/research with PyTorch or TensorFlow; simple CUDA projects a plus. - Familiarity with Grafana/Prometheus/OpenTelemetry or similar tooling. Why CoreWeave? At CoreWeave, we work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: - Be Curious at Your Core - Act Like an Owner - Empower Employees - Deliver Best-in-Class Client Experiences - Achieve More Together We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! The base salary range for this role is $92,000 to $135,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). What We Offer The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location. In addition to a competitive salary, we offer a variety of benefits to support your needs. The benefits below reflect our US-based offerings for full-time employees; for roles in other locations, benefits vary and are shared during the hiring process. These include: - Medical, dental, and vision insurance - 100% paid for by CoreWeave - Company-paid Life Insurance - Voluntary supplemental life insurance - Short and long-term disability insurance - Flexible Spending Account - Health Savings Account - Tuition Reimbursement - Ability to Participate in Employee Stock Purchase Program (ESPP) - Mental Wellness Benefits through Spring Health - Family-Forming support provided by Carrot - Paid Parental Leave - Flexible, full-service childcare support with Kinside - 401(k) with a generous employer match - Flexible PTO - Catered lunch each day in our office and data center locations - A casual work environment - A work culture focused on innovative disruption California Applicants California Consumer Privacy Act Equal Opportunity & Accommodations CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information. As part of this commitment and consistent with the Americans with Disabilities Act (ADA) , CoreWeave will ensure that qualified applicants and candidates with disabilities are provided reasonable accommodations for the hiring process, unless such accommodation would cause an undue hardship. If reasonable accommodation is needed, please contact: careers@coreweave.com . Export Control Compliance This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. CoreWeave may, for legitimate business reasons, decline to pursue any export licensing process.
ML Algorithm Mapping and Performance Engineer, Cor...
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. About The Role The Core ML team develops novel algorithms for efficient large-scale training and inference. We are looking for an engineer who can determine how these algorithms should be mapped to the Cerebras architecture, when they outperform competing approaches, and how their advantages change as models, workloads, and hardware systems scale. You will combine analytical performance modeling, empirical benchmarking, and hands-on prototyping to characterize the efficiency frontiers of emerging ML algorithms. Your work will span kernel-level and end-to-end performance, helping the team reason about trade-offs among model quality, latency, throughput, memory, communication, and compute utilization. This role will directly influence which research ideas Core ML pursues, how those ideas are implemented on current Cerebras systems, and which capabilities should be considered in future generations of hardware and software. Responsibilities - Build analytical and empirical performance models for state-of-the-art ML training and inference algorithms. - Characterize asymptotic behavior and identify how algorithmic trade-offs change with model size, sequence length, batch size, parallelism, and hardware scale. - Construct Pareto frontiers across model quality, latency, throughput, memory footprint, communication, and compute cost. - Develop prototype implementations and benchmarks for the Cerebras WSE and relevant GPU or software baselines. - Analyze system behavior to identify kernel, compiler, runtime, communication, and algorithmic bottlenecks. - Evaluate emerging techniques in areas such as parallel token generation, diffusion and speculative decoding, attention, sparsity, mixture-of-experts, low-precision computation, and distributed training. - Partner with researchers and kernel, compiler, runtime, inference, and architecture teams to recommend high-value implementation and co-design directions. - Develop tools and visualizations that make performance projections, measurements, and design trade-offs understandable across engineering and research teams. - Clearly communicate conclusions, assumptions, limitations, and recommendations through technical reports, presentations, and design reviews. Skills & Qualifications - Bachelor’s, Master’s, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field. - Strong foundation in computer architecture, parallel computing, and systems performance. - Strong understanding of machine learning fundamentals and ML systems, including how model and algorithmic choices affect compute, memory, communication, accuracy, and scaling behavior. - Experience with analytical performance modeling, algorithmic complexity analysis, benchmarking, or system simulation. - Strong analytical and problem-solving skills, including the ability to reason from first principles about compute, memory, and communication costs. - Proficiency in Python and comfort with C++. - Experience profiling and debugging performance in an ML, HPC, CPU, GPU, or accelerator-based system. - Ability to move between mathematical analysis, experimental validation, and practical engineering recommendations. Preferred Skills & Qualifications - Experience with roofline analysis, CPU or GPU simulators, kernel optimization, or hardware–software co-design. - Familiarity with CUDA, Triton, PyTorch, JAX, or open-source LLM training and inference systems. - Understanding of transformer internals, including attention variants, KV-cache strategies, model parallelism, sparsity, quantization, and parallel generation. - Research publications, patents, or significant open-source contributions related to ML systems, computer architecture, or computational efficiency. - Experience evaluating technology choices for future hardware or software architectures. Why Join Cerebras People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras: 1. Build a breakthrough AI platform beyond the constraints of the GPU. 2. Publish and open source their cutting-edge AI research. 3. Work on one of the fastest AI supercomputers in the world. 4. Enjoy job stability with startup vitality. 5. Our simple, non-corporate work culture that respects individual beliefs. Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us! Apply today and become part of the forefront of groundbreaking advancements in AI! Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them. This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice.
Inference ML API SDET
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. About The Team The ML API Quality team is responsible for the confidence behind every production release shipped to Cerebras Inference Cloud. We work closely with platform, infrastructure, ML systems, and product engineering teams to ensure that rapid iteration never comes at the expense of customer trust. Our environment spans distributed cloud systems, multi-region deployments, APIs, orchestration layers, and hardware-backed inference services. We are scaling quickly. The systems are growing in complexity, traffic is increasing rapidly, and release velocity remains high. We need engineers who can build quality systems that scale with the business. About The Role As a Senior Software Engineer in Test for the ML API features team, you will lead testing strategy and execution for AI/ML models, evaluating accuracy, fairness, and performance at scale. You will serve as a key technical leader in delivering and validating all software and hardware components for Cerebras API Features. You will own software components feature integration quality and drive pre-deployment and production validation for Cerebras inference solutions. In this role, you will define and champion best testing practices, establish robust debugging methodologies, and mentor junior engineers while advocating for world-class product quality. Responsibilities - Architect and own end-to-end test strategies for new features, developing scalable tests, frameworks, and tooling to ensure quality. - Lead contributions to industry-standard benchmarks and drive adoption of rigorous evaluation methodologies. - Define and drive automation initiatives to significantly improve internal engineering efficiency and test coverage. - Make strategic decisions around coverage trade-offs, resource requirements, and risk-based testing priorities. - Serve as a technical anchor in a highly agile environment, adapting quickly to shifting priorities while maintaining quality standards. - Mentor and guide junior SDETs on testing methodology, debugging practices, and automation development. - Proactively identify systemic quality gaps and drive cross-functional initiatives to address them. - Lead and facilitate effective technical communication across teams and time zones. Skills & Qualifications - 5+ years of relevant industry experience in software integration, development, or quality engineering. - Deep expertise in automation and programming using one or more languages such as Python, C++, or Go; ability to design and build reusable test frameworks from the ground up. - Proven experience testing compute, machine learning, networking, or storage systems within large-scale enterprise environments. - Strong track record of debugging complex issues across distributed, scaled-out deployments. - Demonstrated ability to lead cross-functional quality initiatives involving product development, product management, customer operations, and field teams. - Excellent verbal and written communication skills, with experience presenting technical findings to both engineering and leadership audiences. - Strong organizational skills, ownership mindset, and ability to drive projects to completion independently. - Experience leading and mentoring engineers across geographically dispersed teams and time zones. Preferred Skills & Qualifications - Hands-on experience with ML workloads including LLM and/or multimodal training or inference. - Deep familiarity with hardware architecture, performance optimizations, compilers, and ML frameworks. - Experience designing test strategies for distributed systems, cloud infrastructure, and security validation. - Experience with microservices deployment, debugging, and orchestration at scale. - Prior experience owning or significantly contributing to a team's quality engineering culture or test infrastructure. Location This role follows a hybrid schedule, requiring in-office presence 3 days per week. Fully remote is not an option. Office locations: Sunnyvale, CA | Toronto, Canada Why Join Cerebras People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras: 1. Build a breakthrough AI platform beyond the constraints of the GPU. 2. Publish and open source their cutting-edge AI research. 3. Work on one of the fastest AI supercomputers in the world. 4. Enjoy job stability with startup vitality. 5. Our simple, non-corporate work culture that respects individual beliefs. Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us! Apply today and become part of the forefront of groundbreaking advancements in AI! Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them. This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice.
Member of Technical Staff - Post Training
ABOUT BLACK FOREST LABS We're the team behind Latent Diffusion, Stable Diffusion, and FLUX — foundational technologies that changed how the world creates images and video. Our models power the tools used by millions of creators, developers, and businesses worldwide, and FLUX is among the most advanced generative systems in the world. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we're scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. WHY THIS ROLE Post-training is where a foundation model becomes a product. In this role, you'll own the post-training pipeline for our multimodal models end to end — from data strategy and reward modeling to preference optimization, distillation, and safety tuning — across image, editing, and video. You'll drive measurable gains in model quality, build the infrastructure that lets the whole research team iterate fast, and push the state of the art in what it means to align a generative model to human intent. This is a Staff / Senior IC role. We're looking for someone who has shipped post-training for a frontier model before and wants to do it again. WHAT YOU'LL WORK ON - Own the full post-training pipeline end to end — from data curation and reward modeling through fine-tuning, preference optimization, distillation, safety tuning, evaluation, and deployment - Advance techniques across the post-training stack: SFT, RLHF, RLAIF, DPO, preference learning, and reward modeling to align models with human intent and aesthetic judgment - Work across modalities: text-to-image, image editing, multi-reference, and video post-training - Build personalization and customization capabilities that let users adapt our models to their own creative style - Design and maintain high-throughput fine-tuning and evaluation infrastructure to support rapid iteration across the research team - Identify quality and alignment gaps through rigorous evaluation, then close them through targeted research and engineering WHAT WE'RE LOOKING FOR - You've owned post-training for a frontier generative model through release (SFT, preference optimization (DPO or RLHF), distillation, safety tuning) with measurable quality wins on human prefs or standard benchmarks - Deep experience across the post-training stack, not just one slice: reward modeling, preference learning, RLHF/RLAIF, and personalization - Comfortable working across modalities: text-to-image, image editing, multi-reference, and ideally video - Strong PyTorch fluency; you write research code that others can build on - Experience with distillation (LADD, DMD, consistency models, or similar) or with building high-throughput eval pipelines is a strong plus - Bias toward shipping: measurable model-quality improvements that reach users, not just papers HOW WE WORK TOGETHER We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process. Everything we do is grounded in four values: - Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful. - Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task. - Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect. - Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos. If this sounds like work you’d enjoy, we’d love to hear from you. Base Annual Salary: EU €130,000-€340,000 + Equity Note: Our recruitment process uses AI-assisted tools to help manage and organize applications. All hiring decisions are always made by our team.
2027 Internship Behavior ML Engineer, Learned Mani...
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. ABOUT THE ROLE & TEAM Teaching a 40-ton excavator to move like an expert operator is a very different problem from teaching a car to stay in its lane. The motions are multimodal, there are many good ways to swing, dig, and dump and the consequences of getting them wrong are measured in cubic yards and bent steel. Bedrock's Behavior ML team builds the learned policies that decide what our machines actually do, and diffusion policies are a central bet: models that can represent the full distribution of expert behavior instead of averaging it into mush. As our Behavior ML intern, you'll help train those policies and build the evaluation that tells us whether they're genuinely better working alongside two hosts who sit on both the training and evaluation sides of the problem. WHAT YOU'LL DO - Train and iterate on diffusion flow matching, and related behavior policies using real fleet demonstration data and simulated rollouts - Run architecture, conditioning, and hyperparameter explorations, and turn the results into clear findings the team can build on - Build and improve evaluation pipelines that score behavior models on task success, smoothness, safety margins, and operator-likeness - Investigate failure modes - distribution shift, mode collapse, out-of-distribution scenes and propose fixes - Work with the simulation and eval teams to make sure offline metrics actually predict on-machine performance - Contribute to the shared training codebase with clean, reviewed, reproducible work - Share results regularly with the behavior, controls, and autonomy teams WHAT WE'RE LOOKING FOR REQUIRED - Currently pursuing a BS, MS, or PhD in computer science, robotics, machine learning, or a related field or bringing equivalent research or industry experience - Strong Python and hands-on experience training models in PyTorch (or equivalent) - Working understanding of generative modeling diffusion models, flow matching, VAEs, or similar and of imitation learning or behavior cloning - Experience running and interpreting real training experiments: you know how to tell a real improvement from noise - Clear communication, you can explain what you tried, what happened, and what you'd do next PREFERRED - Published work or substantial project experience in diffusion, flow matching, robot learning, or imitation learning - Experience training and building Vision Language Action (VLA) models - Experience training Reinforcement Learning (RL) policies for robot manipulation - Experience evaluating policies in simulation and reasoning about the sim-to-real gap - Familiarity with large-scale training infrastructure and experiment tracking tooling - Exposure to robotics, autonomous vehicles, or other physical-world control problems - Interest in construction, earthwork, or heavy equipment - no prior experience required 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.
Software Engineer - Model Performance Systems
ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We are looking for Software Engineers to join our team. This is a specialized, high-impact role sitting at the intersection of high-performance computing (HPC) and Large Language Model (LLM) engineering. You will not just be building the automated "speedometer and diagnostic" suite for our next-generation AI infrastructure; you will be defining the roadmap, driving key technical decisions, and taking full ownership of the future of this work. RESPONSIBILITIES - Benchmarking: Evaluate, run and automate standard LLM quality benchmarks (GSM8K, MMLU) alongside custom performance suites for specific workloads (e.g., long-context window, KV cache reuse, disaggregated serving). - DevEx Improvement: Develop and maintain internal GPU-enabled development environments (similar to GitHub Codespaces). You will ensure the team has seamless, high-performance "dev machines" optimized for model experimentation. - Tool Development: Build and contribute to open-source tools such as InferenceMAX and genai-bench to automate model evaluation, benchmarking and analysis. - System Profiling: Use profilers like PyTorch Profiler, NVIDIA Nsight Systems and py-spy to collect performance profiles, identify bottlenecks, and debug the compute/networking stack. - Monitoring & Observability: Develop real-time dashboards and alerts to monitor system health, model startup times, and runtime performance. - Continuous Integration: Automate performance testing via CI/CD pipelines to catch regressions and build release workflow automation for the model runtimes stack. - Optimization Automation: Build tools to find the "Pareto frontier"—identifying the absolute best configuration (latency vs. cost vs. quality) for a given model and workload. REQUIREMENTS This is a mid-senior, high leverage role. We care about your technical depth, strong communication skills to drive cross-team efforts, ability to navigate vague requirements and mentor other engineers. We want to talk to you if you have: - A Love for Systems & Hardware: You aren’t just interested in the AI; you want to understand GPU memory subsystems, InfiniBand, and how data moves across a cluster. - An Automation Mindset: You believe that if a task has to be done twice, it should be scripted. You have a passion for stress-testing and fuzzy testing to find the "breaking point" of a system. - Mathematical Curiosity: A desire to understand the underlying math of Transformers and how it translates into FLOPs and memory requirements. - Technical Toolkit: Familiarity with Python, and an eagerness to master the NVIDIA software stack. C++ familiarity is good to have. WHY THIS ROLE - Direct Impact: Your tools will be the gatekeeper for what defines "good" performance for our customers. - Deep Learning (Literally): You will gain world-class expertise in GPU orchestration and LLM inference that few engineers in the industry possess. - High Ownership: As the lead of a small team, you will have the autonomy to build tools from scratch and contribute to open-source projects. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Forward Deployed Engineer (Training)
ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Forward Deployed Engineers work directly with the largest and fastest-growing AI companies in the world, owning their technical outcomes on Baseten and taking on the hardest problems in serving and improving models at scale. The work spans the model lifecycle: inference, post-training, and the systems that tighten the loop between them. - Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled. - Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem. - Design the evals and benchmarks that isolate where quality or performance falls short, then close the gap yourself, whether that means optimizing inference, improving the model through post-training, or reworking the eval itself. - Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships. - Build internal systems so that each engagement is faster than the last. This includes tooling and automation for eval and deployment infrastructure, and the recipes and reference implementations that make the product more self-serve. - Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourself. - Do all of this across multiple accounts at once, sequencing the work, pulling in the right people at the right time, and keeping customers and internal stakeholders aligned on status and risk. REQUIREMENTS - Minimum 1-2 years of software engineering experience, shipping and maintaining code in large production systems, ideally with breadth across the stack - Experience debugging complex production issues - working through logs, metrics, and traces to root-cause problems in unfamiliar systems - Confidence owning ambiguous technical problems. This includes triaging, making decisions under uncertainty, and knowing when to pull in other engineers who own the underlying systems - Motivation beyond pure engineering. An interest in wanting to work directly with customers, understand their problems firsthand, and influence the product - Clear communication on complex technical topics, whether you're talking to a customer's engineers or their leadership - Genuine curiosity about AI inference and training, and a drive to become an expert in the infrastructure powering it - Willingness to respond to customers outside regular working hours and participate in an on-call rotation - Excitement about solving problems for some of the largest and fastest-growing companies in the world running mission-critical AI workloads WHAT YOU'LL BRING We don't expect any one person to cover all of this - the strongest candidates may spike in one or two of the following: - Depth in a core infrastructure domain such as storage systems or networking (anywhere from the cloud layer to cluster interconnects like InfiniBand and RoCE) - Experience operating distributed compute platforms like Kubernetes, Slurm, or Ray, especially for GPU workloads - A detailed understanding of LLM architectures and modern inference engines like vLLM, TensorRT-LLM, or SGLang - The ability to profile and optimize GPU workloads, in training or serving - Hands-on experience with post-training techniques like SFT and RL, or a broader deep learning background plus fluency in a tensor computation library like PyTorch or JAX - Operational depth - running on-call, leading incident response, debugging distributed systems under pressure BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
AI Engineer
ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE Are you the person on your team who builds the agent everyone else ends up using? We're looking for an AI Engineer to join our Training Product team and do that at Baseten. You'll build AI-driven product features for the customers training and post-training frontier models on our platform, and you'll raise the ceiling on how Baseten itself uses AI internally, turning manual workflows into agentic ones that make every other team faster. You'll work directly with our research engineers to scope and build products, taking ideas from a research loop that already works internally to something customers can run themselves. This is a hands-on role with real autonomy. You'll pick the problems worth solving, build the harnesses, execution flows, and guardrails that make AI systems reliable, and own the results. If you've been shipping agents and want that to be the job, let's talk. EXAMPLE INITIATIVES Take a look at these blog posts written by members of our team: - Baseten Training: an autoresearch substrate https://www.baseten.co/blog/baseten-training-an-autoresearch-substrate/ - Introducing Baseten Loops https://www.baseten.co/blog/introducing-the-baseten-loops-sdk/ - Harnesses are everything. Here's how to optimize yours. https://www.baseten.co/blog/harnesses-are-everything-heres-how-to-optimize-yours/ - Building with NVIDIA Nemotron 3 Ultra and LangChain Deep Agents Code on Baseten https://www.baseten.co/blog/nvidia-nemotron-3-ultra-and-langchain-deep-agents-on-baseten/ RESPONSIBILITIES - Build and ship agentic product experiences, including chat-style and assistant-like interfaces, from prototype to GA. - Design the harnesses, execution flows, and guardrails that make AI systems reliable in production. - Build internal automation and AI tooling that measurably increases the velocity of engineering, research, and go-to-market teams. - Partner with research engineers to scope product opportunities out of internal research workflows and turn them into customer-facing features. - Define and instrument evals so you know whether a change actually improved output quality. - Work throughout the stack (API layer, backend, agent orchestration, frontend) to implement features end to end. - Use Baseten's own training and inference products yourself to develop intuition around customer workflows. - Identify where AI can replace manual process across the company and build the thing rather than write the proposal. - Fix bugs and resolve customer issues with urgency. REQUIREMENTS - 5+ years of experience building and shipping software applications. - Demonstrated experience building AI or LLM-powered products, agents, or agentic workflows that real users depend on. - Strong software engineering fundamentals and the ability to clear a real technical bar, not just prompt well. - Ability to build accurate mental models of how systems work under the hood, including the models and harnesses you're building on. - Proficiency in Python, with fluency in at least one other language. - Comfort working autonomously in a fast-moving environment with limited structure. - Ability to move between customer-facing product work and internal tooling and automation. - Strong communication skills, with the ability to bridge technical depth and business needs. NICE TO HAVE - Experience as a founding engineer or early employee at a startup. - Experience building evals, agent observability, or tooling for non-deterministic systems. - Familiarity with agent frameworks and harnesses (LangChain, Claude Code, Codex, OpenCode, MCP). - Experience with model development methods like supervised fine-tuning, reinforcement learning, synthetic data generation, LoRA, and full fine-tunes. - Frontend fluency. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Staff AI Engineer - Agent Architecture & Behavior
Artisan · Full-time · In person in San Francisco Base salary: $250,000–$325,000 USD annually. Equity: 0.15%–0.30%. US visa sponsorship available. BUILD SOMETHING NEW AT THE FRONTIER OF APPLIED AI At Artisan, we're working on a new, ambitious project that will push the boundaries of what agentic AI can do. We're keeping the product details private ahead of launch, but we can tell you this: the technical problems are substantial, the scope for invention is real, and this hire will shape the core technology. We're looking for a hands-on technical lead to design and build the underlying AI architecture. You'll work across agent behavior, complex multi-agent systems, tool use, context, and evaluation, taking promising ideas through to dependable production software. This is an individual-contributor role with broad technical ownership. You'll make consequential architecture decisions, write the hardest parts of the system, and work closely with our existing engineers and leadership. You should enjoy both exploring an uncertain problem and doing the detailed engineering required to make a solution work. WHAT YOU'LL OWN - Agent architecture and behavior. Design and implement agent execution loops, planning strategies, tool interfaces, and verification. Turn ambiguous technical requirements into clear system boundaries and working code. - Multi-agent systems. Build delegation, coordination, context sharing, and result synthesis. Handle concurrent work, conflicting updates, cancellation, and stale results. Establish when a multi-agent approach improves on a simpler baseline. - Reliable execution. Make complex, stateful workflows resilient to interruptions and partial failures. Build checkpoints, recovery strategies, and appropriate human intervention into the architecture. - Context, memory, and reusable methods. Improve retrieval, context construction, persistent state, and skill representation. Investigate how systems can use feedback and prior experience to perform better without introducing regressions. - Evaluation and experimentation. Build realistic evaluations, analyze task trajectories, and turn observed failures into measurable improvements. Compare approaches using quality, reliability, latency, and cost. - Model and tooling decisions. Evaluate models and emerging techniques, prototype promising approaches, and make informed build-versus-buy decisions. Choose tools because they solve the problem, and be willing to replace them when the evidence changes. - Technical leadership. Set engineering standards, review important design decisions, and help the team implement a coherent AI system. Stay close to the product and accountable for what ships. You'll partner with product and infrastructure engineers on production services, integrations, secure execution, and observability. You'll own the AI architecture and its effectiveness, with implementation shared across the team. WHAT WE'RE LOOKING FOR - You have personally built and shipped a substantial agentic system. Production use or rigorous, reproducible open-source work matters more than the name of a framework or employer. - You have deep practical experience with LLM tool use, planning, context engineering, and evaluations. You have implemented multi-agent coordination or substantial parallel agent/tool execution and can explain its failure modes. - You have hands-on experience with browser or computer automation in an agentic system, including observing state, verifying effects, and recovering when an interface or execution path fails. - You are an excellent software engineer in Python, TypeScript, or a comparable language. You are comfortable with asynchronous services, state machines, persistence, concurrency, retries, and cancellation. - You know which decisions belong to a model and which guarantees must be enforced in code. You can reason carefully about permissions, untrusted inputs, uncertain external outcomes, and human approvals. - You can design meaningful experiments, debug real system behavior, and explain what improved, why it improved, and where the evidence is still weak. - You can take technical ownership of an unclear problem, work effectively with other engineers, and ship with urgency and care. USEFUL ADDITIONAL EXPERIENCE Depth in agent memory and retrieval, skill acquisition, reinforcement learning or post-training, trajectory datasets, sandboxed execution, distributed systems, inference optimization, or multimodal and voice models would be valuable. We expect strong foundations and particular depth in a few areas, rather than prior specialization in every one. There is no required degree, publication record, previous employer, or agent framework. We're hiring for demonstrated engineering ability, judgment, and ownership. HOW WE WORK This role is based in our San Francisco office. Expect a small team, short feedback cycles, direct communication, and high standards. We value people who move quickly, take responsibility for the result, surface problems early, and change their minds when the evidence calls for it. You'll have substantial freedom to explore ambitious technical ideas and the responsibility to turn the best ones into software that works. We'll discuss the project in more detail during the interview process. INTERVIEW PROCESS Our conversations will center on systems you've built, a practical agent architecture and debugging exercise, and how you work with a team. When applying, tell us about one agentic system you personally owned: what you built, the hardest failure you fixed, and how you measured the improvement. A project link, technical write-up, or open-source contribution is welcome. You can discuss confidential work without sharing proprietary material. The base salary range is $250,000–$325,000 USD annually, plus equity. The final offer will reflect relevant experience, demonstrated skills, and the scope of responsibility. ABOUT ARTISAN Artisan builds AI employees that take on real work. Our first three are Ava, our outbound AI BDR; Aaron, our inbound AI SDR; and Aria, our AI account executive. Our broader mission is to build AI employees that can take responsibility for work across many roles and industries.
Perception Engineer - Defense (All-Domain)
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co http://applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions ABOUT THE ROLE The Perception Engineer role at Applied Intuition Defense is a hands-on, full-stack, high tempo position where you will be part of an expert team rapidly pursuing the second (99%) and third nine (99.9%) of perception model performance across maritime, land, air-to-surface, and surface-to-surface domains, in EO and IR. We develop our own custom detector and classifier architectures, we’ve developed our own comprehensive MLOps tooling allowing us to rapidly iterate at the scale of millions of images and videos, we make our own industry-leading labeling software, and we manage in-house labeling on a daily basis. We do all of this using frontier AI and large token budgets. If something needs to be built to get the job done, we will build it. If you have extensive modeling experience with transformers, vision-language models, diffusion models, and video model training pipelines, we need you. If you have deployed production ML models into the real world or optimized them heavily, we need you. AT APPLIED INTUITION DEFENSE, YOU WILL: - Develop perception models that find and classify targets with an error rate of <1/100 - Deploy perception models to Jetson Orin+ boards and optimize models for better-than-realtime performance. - Build large foundation models for high-accuracy assisted data labeling. - Build tooling across the entire perception stack: labeling tools, simulation. - Work with the best and most competitive AI talent in the world through collaboration with Applied Intuition self-driving staff. - Scale up datasets using a variety of state-of-the-art data generation techniques including simulation, diffusion, and generative AI. - Create and maintain highly optimized models and inference software providing model results at maximum attainable frames per second (FPS) on Jetson-like hardware. - Collaborate across the hardware, sensor, tracking, autonomy, and testing teams to ensure seamless deployment in on-site DoD testing and demonstration events. WE’RE LOOKING FOR SOMEONE WHO HAS: - MS or PhD in Computer Engineering, Robotic Engineering, Computer Science, or equivalent OR 5+ years of relevant experience working with simulation, machine learning, and ML infrastructure. - Proficiency in training ML models in PyTorch on multi-machine, multi-GPU systems. - Experience in optimizing and deploying machine learning models to edge devices. - Strong experience with agentic coding with proven experience in Python and/or cpp. - Experience leveraging modern AI-powered development tools (e.g., GitHub Copilot, Cursor) to accelerate the creation of robust, well-tested systems. - Adeptness with remote software development, the ability to handle and process large datasets, and a capacity to learn new software and algorithms as needed with little supervision . Travel Requirements: - Must be willing to travel as projects require, usually for SW/HW integration and/or demonstrations; estimated average travel is 2-5 days every other month (10-20%) - Interested candidates should also inquire about relocation assistance to Applied Intuition Defense facilities near Destin, FL, Washington, D.C., or San Diego, CA Security Requirements: - Must be a U.S. Citizen - Must hold or be eligible to obtain and maintain a U.S. security clearance For more information about U.S. security clearances: click here https://www.dcsa.mil/Personnel-Security/Background-Investigations-for-Applicants/Investigations-Clearance-Process/. NICE TO HAVE: - PhD in Computer Vision, Machine Learning, or related field with strong academic contributions - 5+ years of experience building and deploying perception models on real-world hardware - A background in generating datasets using simulation engines like Unreal Engine, NVIDIA Omniverse, Blender, or 3D asset creation tools - Experience with deeply optimizing Transformer-based models for edge devices like NVIDIA Jetson boards - Familiarity with Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL), including integration and test experience on common COTS hardware devices (e.g., NVIDIA Jetson, Raspberry Pi5) Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment. Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base salary offered to a successful candidate will additionally be influenced by a variety of factors including experience, credentials & certifications, educational attainment, skill level requirements, interview performance, and the level and scope of the position. Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!
Software Engineer, ML Developer Experience
About Anyscale: At Anyscale https://www.anyscale.com/, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We're commercializing Ray https://docs.ray.io/en/latest/, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI https://thenewstack.io/how-ray-a-distributed-ai-framework-helps-power-chatgpt/, Uber https://www.uber.com/blog/horovod-ray/, Spotify https://engineering.atspotify.com/2023/02/unleashing-ml-innovation-at-spotify-with-ray/, Instacart https://www.youtube.com/watch?v=3t26ucTy0Rs, Cruise https://www.youtube.com/watch?v=gj0BqvfX_wI, and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we're building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition https://www.wsj.com/articles/ai-startup-anyscale-adds-99-million-to-andressen-horowitz-led-funding-round-11661254200 with $250+ million raised to date. About the role Anyscale is looking for a Software Engineer to join the ML Developer Experience (MLDevX) team. MLDevX owns the experience layer of the Anyscale platform: the interfaces through which users and coding agents discover, configure, run, observe, debug, and productionize AI workloads. Every user journey crosses this layer through the CLI, SDKs, APIs, UI, Workspaces, MCP, or the workflows and integrations built on top of them. Together, these form the user’s primary interface into Anyscale, turning distributed computing from a systems problem back into a coding problem. We build the common contracts, tools, control-plane services, and architecture that power these surfaces. You will work across the stack from developer tooling to cloud infrastructure and the Ray runtime. Manage long-running operations and make failures across jobs, tasks, actors, nodes, and GPUs easier to diagnose. The systems you build must scale with the platform, remain predictable through failures, and be intuitive for developers, programmable for applications, and operable by coding agents. This is a high impact individual-contributor role with end-to-end ownership. You will work directly with users and field teams to identify high leverage problems, shape the product and technical design, and build and operate the solution. The scope spans the AI workload loop - data preparation, fine-tuning and post-training, serving, evaluation, and iteration as well as the developer loop: code, submit, monitor, debug, optimize, and re-submit. We are looking for someone with strong product judgment, a willingness to understand the user base, and the technical depth to build high quality software for everyone from a developer learning Ray for the first time to an AI-native company or enterprise running production workloads at scale. A SNAPSHOT OF PROJECTS YOU MAY WORK ON - Build the next generation of developer tooling and MLOps capabilities on Ray, designed for both developers and coding agents. - Develop an agent-first CLI and cohesive SDK, API, and MCP surfaces with self-discovery, structured errors, dry-run support, and consistent behavior across platform resources. - Work across the Anyscale Workspaces stack to improve the path from local code to distributed execution, including environments, dependencies, images, authentication, workload submission, and debugging. - Build cohesive experience, tools and frameworks for the AI development lifecycle, including data preparation, fine-tuning and post-training, evaluation, production serving, dataset management, experiment tracking, and lineage. - Build the path from a trained model to a reliable production endpoint, including model registration, deployment workflows, performance benchmarking, and LLM-specific service metrics. - Surface observability across the CLI, SDK, UI, and agent-facing interfaces so users can diagnose failures across jobs, tasks, actors, nodes, and GPUs. - Design open integrations using durable standards such as OpenAI-compatible APIs and OpenTelemetry, along with stable Jobs and Services interfaces for external orchestrators. - Design and operate the highly available backend services and platform architecture that power these capabilities across serverless and bring-your-own-cloud environments. - Work closely with users and field teams to scope, ship, and iterate on the product, and collaborate with distributed systems and machine learning experts to push the boundaries of AI infrastructure. WE'D LOVE TO HEAR FROM YOU IF YOU HAVE - Bachelor's degree in Computer Science, Engineering, or equivalent practical experience - 5+ years of experience writing high-quality production code - A solid background in algorithms, data structures, and system design - Experience working with modern machine learning tooling — PyTorch, MLflow, data catalogs, and similar - Hands-on experience building and operating highly available services in production - Strong product instincts and a track record of shipping developer-facing tools that people choose to use BONUS POINTS IF YOU HAVE - Experience building and maintaining open-source projects - Experience building and operating machine learning infrastructure in production - Experience building highly available serving systems - Experience using Ray Anyscale Inc. 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. Anyscale Inc. is an E-Verify company and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1Kt2S6_k_SjxaEdGowH4rngVdg2ApAQV3/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1K3Nz72xgsU2hngnVUEu53wEeZjbAMbnZ/view?usp=sharing.
Staff TLM, Machine Learning, Sentry Tower
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 Counter Intrusion MSE team develops systems that provide force protection capabilities, monitoring the perimeter of secure areas, land or sea, for approaching people, vehicles, and vessels. We live in a world where security officers are increasingly overwhelmed by sensor data feeds. Our products leverage advanced sensor fusion and autonomy to seamlessly render activity in the environment to Lattice's common operating picture. This relieves operators of our system from a ton of burden and allows our customers to get more done with fewer personnel. Increasingly, Counter Intrusion MLEs are working on projects to bring customers' existing legacy security systems into the Lattice ecosystem. Key to this effort is building scalable software solutions so that we can service many customers without requiring bespoke software for each one. The Counter Intrusion team is responsible for the development, testing, deployment, and sustainment of our family of systems. We work closely with other teams from product, engineering, sales, logistics, operations, and mission success. About the Role We are looking for a Staff ML Tech Lead Manager to apply the latest research to solve our toughest problems. This role will be responsible for owning the whole machine learning stack for the Counter Intrusion team. You will design and train multi-sensor object detection models for perception on edge compute devices that push the boundaries of what's possible with our sensors. You will develop learning algorithms to optimize the behavior of autonomous systems. You will also be responsible for the end-to-end design, implementation, and performance of the ML stack, including the infrastructure for data collection and training. Your work will directly impact Anduril's ability to deliver cutting-edge defense technology, with the opportunity to identify and develop novel ML applications across our product portfolio. Responsibilities - Propose and prototype innovative solutions to solve real-world problems, leveraging the latest state-of-the-art techniques in the field - Develop and maintain core ML pipelines - Train and deploy deep learning models for real-time applications - Collaborate cross-functionally with camera, systems and labeling teams - Curate datasets for evaluating performance and comparing performance trends over time - Provide technical mentorship to other junior ML engineers Required Qualifications - MS or PhD in Machine Learning, Robotics or Computer Science, with emphasis on Computer Vision - BS in Computer Science, Machine Learning, Electrical Engineering, or related field - 6+ years of experience developing, benchmarking and optimizing ML algorithms on large-scale datasets - Strong Deep Learning and CV background - Proficiency in C++ development in a Linux environment - Experience with Python development and deep learning frameworks such as PyTorch, JAX and TensorFlow - Experience deploying models with TensorRT and ONNX - Optimize on-device inference and vision kernels across CPU/GPU/NPU - Track record of developing and deploying CV models from R&D to production - Experience writing and maintaining automated continuous integration tests - Knowledge of system profiling and tuning for latency, memory and power efficiency - Ability to conduct experiments, ablation studies and create highly detailed reports - Eligible to obtain and maintain a U.S. Secret security clearance Preferred Qualifications - Experience in one or more of the following: - Object Detection, Object Tracking, Instance Segmentation, Semantic Segmentation, Semantic Change Detection, Depth Estimation, Model Pruning and Compression - Experience in one or more of the following: - Visual Odometry, SLAM, Multi-view Geometry, Camera Models, RGB-D and LIDAR Sensor Fusion, Optical Flow - Experience troubleshooting and analyzing remotely deployed software systems - 1 year of experience in a technical leadership role US Salary Range $220,000 - $330,000 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.
AI Software Tech Lead, ML
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 AI Software Tech Lead, ML role within Aidoc’s AI Engineering group, focused on building and scaling the infrastructure behind advanced AI solutions in computer vision and NLP for medical imaging. The role combines hands-on software development, technical leadership, and close collaboration with cross-functional teams to transform AI innovation into scalable, high-impact products. Responsibilities - Design, develop and lead SW development of infrastructure for innovative AI algorithms for computer vision and NLP in the context of medical imaging. - Collaborate with cross-functional teams, including researchers and product managers, to drive the development of new AI products and features. - Work in a unique professional culture applying the full range of software engineering capabilities in the core parts of the A.I. systems - Develop and maintain high-quality, scalable, and efficient code. - Take initiative in exploring and adopting of new technological advances in all relevant domains (for example, scalable cloud based systems, DL and CV SW frameworks, Large scale ML systems) Requirements - BA/BSc/MSc in Computer Science, Engineering or a related field. - 7+ years of experience in developing large scale production systems – including architecture design, research, development and testing. - Deep understanding of Computer Science fundamentals – object-oriented design, code optimization, and complexity - Passion for building generic and robust software architecture - Passion for elegant and effective technological solutions, and a love for clean code - Strong experience with Python and advantage to deep learning frameworks such as TensorFlow, PyTorch, etc. - Strong ability to lead end-to-end development processes - Advantage: Experience in developing AI and machine learning solutions, with a focus on computer vision and NLP. - Strong communication and collaboration skills, with the ability to work effectively with a cross-functional team 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.
AI Senior 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 Senior AI Software Engineer role within Aidoc’s AI Engineering group, focused on building and scaling the infrastructure behind advanced AI solutions in computer vision and NLP for medical imaging. The role combines hands-on software development, technical leadership, and close collaboration with cross-functional teams to transform AI innovation into scalable, high-impact products. Responsibilities - Design, develop and lead SW development of infrastructure for innovative AI algorithms for computer vision and NLP in the context of medical imaging. - Collaborate with cross-functional teams, including researchers and product managers, to drive the development of new AI products and features. - Work in a unique professional culture applying the full range of software engineering capabilities in the core parts of the A.I. systems - Develop and maintain high-quality, scalable, and efficient code. - Take initiative in exploring and adopting of new technological advances in all relevant domains (for example, scalable cloud based systems, DL and CV SW frameworks, Large scale ML systems) Requirements - BA/BSc/MSc in Computer Science, Engineering or a related field. - 7+ years of experience in developing large scale production systems – including architecture design, research, development and testing. - Deep understanding of Computer Science fundamentals – object-oriented design, code optimization, and complexity - Passion for building generic and robust software architecture - Passion for elegant and effective technological solutions, and a love for clean code - Strong experience with Python and advantage to deep learning frameworks such as TensorFlow, PyTorch, etc. - Strong ability to lead end-to-end development processes - Advantage: Experience in developing AI and machine learning solutions, with a focus on computer vision and NLP. - Strong communication and collaboration skills, with the ability to work effectively with a cross-functional team 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 Software Engineer, AI/ML Platform
Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential. About The Role Join the team building the machine learning platform to power fleet-scale humanoid robotics. As a senior engineer on the ML Infrastructure and Platform group, you will help architect and build the foundational infrastructure for AI and machine learning operations at Agility. This includes the platform layer for data collection and processing, training, sim and real evaluation, and model management and observability. Your work will empower our AI teams across perception, controls, skills, and innovation to build and deploy next-generation robot foundation models and end-to-end policies for humanoid robots by providing tools to develop and operationalize machine learning at scale. Key Responsibilities Execution and Technical Ownership - Contribute to the design and implementation of the ML platform for orchestrating the end to end AI flywheel of data processing, training, evaluation, and deployment - Develop reliable workflows across cloud compute, Kubernetes, and continuous automation - Build core infrastructure components such as the model registry, feature store and experiment tracking tooling. - Own developer-facing APIs and CLI tools that make ML workflows simple and reproducible. - Implement the CI/CD lifecycle for ML that enable continuous retraining, automated testing, and seamless model delivery to production environments Collaboration - - Work closely with the Staff ML Infra Engineer and cross-functional stakeholders (AI researchers and robotics engineers) to understand requirements and translate them into scalable solutions/systems. - Partner with data platform engineers to integrate ML orchestration and metadata tracking tools with our existing data lake and pipelines. Engineering Excellence, Growth and Impact : - - Apply MLOps best practices: reproducibility, lineage, rollback, monitoring and governance. - Mentor junior engineers and influence the broader cloud platform organization’s roadmap. - Contribute to internal discussions on platform architecture, reliability, and scalability alongside the broader ML and data platform team What We’re Aiming For (MLOps Level 2) - Version-controlled ML pipelines (data, code, and config) - Automated and reproducible model training and evaluation - Continuous integration and delivery for ML workflows - Centralized experiment tracking and performance visualization - Standardized model packaging and deployment to production - Monitoring of models post-deployment Required Qualifications - 5+ years of software engineering experience, with at least 2+ years working on ML infrastructure, data platforms or MLOps systems in production environments. - Experience building and maintaining components of modern ML platforms—such as experiment tracking, model registries, training pipelines, or deployment systems - Familiarity with orchestration and tracking tools (MLflow, WandB, Airflow, Kubeflow, etc.) - Proficiency with cloud-native platforms (AWS, GCP, or Azure), containers, and IaC (e.g., CDK, Terraform) - Hands-on experience with processing or modeling multimodal data(sensor logs, camera streams, behaviour traces etc). - Comfortable collaborating cross-functionally with research scientists, data engineers, and robotics/autonomy teams to ship infrastructure used by others Bonus Qualifications - Experience with robotics, autonomous vehicles, drones or embedded ML. - Contributions to open-source ML infrastructure or MLOps tooling a plus. Why This Role? - Build from the start Join at a pivotal moment - help shape the ML platform layer as its being defined, not inherited. - High impact : Your work will directly enable faster, safer, and more intelligent robotic behaviors at scale - Technical Frontier : You will work directly on enabling the next frontier of AI in real production settings - Remote-friendly with a strong engineering culture and a fully distributed team. The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future. Anticipated Base Salary Range $197,000 - $307,000 USD In addition to base pay, our competitive total rewards package consists of the following for full-time employees: - 401(k) Plan: Includes a 6% company match. - Equity: Company stock options. - Insurance Coverage: 100% company-paid medical, dental, vision, and short/long-term disability insurance for employees. - Benefit Start Date: Eligible for benefits on your first day of employment. - Well-Being Support: Employee Assistance Program (EAP). - Time Off: - Exempt Employees: Flexible, unlimited PTO and 12 company holidays, including a winter shutdown. - Non-Exempt Employees: 10 vacation days, paid sick leave, and 12 company holidays, including a winter shutdown, annually. - On-Site Perks: Catered lunches four times a week and a variety of healthy snacks and refreshments at our Salem and Pittsburgh locations. - Parental Leave: Generous paid parental leave programs. - Work Environment: A culture that supports flexible work arrangements. - Growth Opportunities: Professional development and tuition reimbursement programs. - Relocation Assistance: Provided for eligible roles. - Annual Discretionary Bonus: Provided for eligible roles. All of our roles are U.S.-based. Applicants must have current authorization to work in the United States. Agility Robotics is committed to a work environment in which all individuals are treated with respect and dignity. Each individual has the right to work in a professional atmosphere that promotes equal employment opportunities and prohibits unlawful discriminatory practices, including harassment. Therefore, it is the policy of Agility Robotics to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Agility Robotics prohibits any such discrimination or harassment. Agility Robotics does not accept unsolicited referrals from third-party recruiting agencies. We prioritize direct applicants and encourage all qualified candidates to apply directly through our careers page. If you are represented by a third party, your application may not be considered. To ensure full consideration, please apply directly.
Senior Director, AI
Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physically demanding and repetitive tasks while enabling workers to focus on higher-value work. With industry-leading safety standards and years of proven deployment data, we're pioneering a new era of automation that enhances human potential. Overview The Senior Director of AI will be a pivotal leader within our cutting-edge humanoid robotics company, driving the strategic direction and execution of all artificial intelligence and machine learning initiatives. This role requires a knowledgeable technologist and a skilled manager capable of championing and leading our existing high-performing team to develop the AI foundation for the next generation of humanoid robots. The successful candidate will be instrumental in defining the AI architecture, ensuring ethical implementation, and delivering robust, real-time AI capabilities that enable our robots to interact intelligently and safely with the world. Core Responsibilities and Deliverables Strategic Leadership and Vision - Define and champion the overall AI strategy and roadmap in alignment with the company's product vision and business goals. - Lead the selection and integration of advanced machine learning models, algorithms, and deep learning techniques relevant to embodied AI (e.g., perception, manipulation, natural language understanding). - Foster a culture of innovation, rigorous research, and responsible AI development that supports the team's ongoing success. - Ensure that all AI systems adhere to the highest standards of safety, privacy, and ethical guidelines. - Partner with Hardware and Robot Software leadership to ensure the sensor suite and embedded compute are optimized for current and future AI model architectures. Technical Execution and Development - Oversee the design, development, and deployment of on-robot and cloud-based AI systems, leveraging the existing team's expertise. - Establish MLOps best practices for efficient model training, deployment, monitoring, and iteration. - Drive research into novel AI solutions for complex robotics challenges, such as real-time planning, human-robot interaction, and adaptive motor control. - Deliver production-ready AI models that meet performance, latency, and reliability requirements for commercial deployment. - Spearhead the Sim2Real strategy, utilizing physics-based simulation (e.g., MuJoCo, Isaac Sim) to train robust policies that transfer seamlessly to hardware. - Accelerate our data flywheel strategy to combine teleoperation, synthetic data, and fleet learning to expand the fleet performance and reliability. Team Building and Hiring - Champion the existing team's talent and represent their needs to senior leadership. - Develop a comprehensive hiring plan to staff and scale the AI team, including defining required roles (e.g., ML Engineers, Data Scientists, AI Researchers). - Lead recruitment, mentorship, and career development for all members of the AI team. - Manage the budget allocated for team salaries, equipment, and necessary infrastructure. Resourcing and Budgeting - Forecast and manage the budget for cloud computing resources (GPUs, TPUs) necessary for large-scale model training and experimentation. - Secure and allocate funding for specialized datasets and data annotation services. - Evaluate and procure necessary software licenses and tools for AI development and simulation. - Regularly report on expenditure and resource utilization to senior leadership. Required Qualifications - Advanced degree (M.S. or Ph.D.) in Computer Science, Robotics, Electrical Engineering, or a related field. - Minimum of 8 years of experience in AI/ML, with at least 5 years in a senior leadership or directorial role. - Demonstrated experience in developing and deploying real-time AI solutions for robotics, autonomous systems, or other mission-critical applications. - Strong expertise in core AI domains, including computer vision, reinforcement learning, and large language models (LLMs) applied to robotics. - Proven track record of managing significant technical budgets and scaling high-performance engineering and research teams. - Experience deploying models on resource-constrained edge devices, balancing inference speed, power consumption, and thermal limits. This is a fully remote role with the option to work hybrid if a commutable distance from our Salem, OR, Pittsburgh, PA, or Fremont, CA offices. The final salary offered to a successful candidate will be dependent on several factors that may include but are not limited to: market location, job-related knowledge, skills, and experience. This range may change based on geographical location and may be modified in the future. Anticipated Base Salary Range $257,000 - $402,000 USD In addition to base pay, our competitive total rewards package consists of the following for full-time employees: - 401(k) Plan: Includes a 6% company match. - Equity: Company stock options. - Insurance Coverage: 100% company-paid medical, dental, vision, and short/long-term disability insurance for employees. - Benefit Start Date: Eligible for benefits on your first day of employment. - Well-Being Support: Employee Assistance Program (EAP). - Time Off: - Exempt Employees: Flexible, unlimited PTO and 12 company holidays, including a winter shutdown. - Non-Exempt Employees: 10 vacation days, paid sick leave, and 12 company holidays, including a winter shutdown, annually. - On-Site Perks: Catered lunches four times a week and a variety of healthy snacks and refreshments at our Salem and Pittsburgh locations. - Parental Leave: Generous paid parental leave programs. - Work Environment: A culture that supports flexible work arrangements. - Growth Opportunities: Professional development and tuition reimbursement programs. - Relocation Assistance: Provided for eligible roles. - Annual Discretionary Bonus: Provided for eligible roles. All of our roles are U.S.-based. Applicants must have current authorization to work in the United States. Agility Robotics is committed to a work environment in which all individuals are treated with respect and dignity. Each individual has the right to work in a professional atmosphere that promotes equal employment opportunities and prohibits unlawful discriminatory practices, including harassment. Therefore, it is the policy of Agility Robotics to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Agility Robotics prohibits any such discrimination or harassment. Agility Robotics does not accept unsolicited referrals from third-party recruiting agencies. We prioritize direct applicants and encourage all qualified candidates to apply directly through our careers page. If you are represented by a third party, your application may not be considered. To ensure full consideration, please apply directly.
Member of Technical Staff, Machine Learning Infras...
ABOUT ABRIDGE Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients. Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems. We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh. THE ROLE As an ML Infrastructure Engineer at Abridge, you’ll play a pivotal role in building and optimizing the core inference infrastructure that powers our machine learning models. Your work will be instrumental in enhancing the scalability, efficiency, and performance of our AI-driven solutions. You will work with our Infrastructure and Research teams to build, deploy, optimize and orchestrate across our AI models. What You'll Do - Design, deploy and maintain scalable Kubernetes clusters for AI model inference and training - Develop, optimize, and maintain ML model serving infrastructure, ensuring high-performance and low-latency. - Collaborate with ML and product teams to scale backend infrastructure for AI-driven products, focusing on model deployment, throughput optimization, and compute efficiency. - Optimize compute-heavy workflows and enhance GPU utilization for ML workloads. - Build a robust model API orchestration system - Collaborate with leadership to define and implement strategies for scaling infrastructure as the company grows, ensuring long-term efficiency and performance. What You’ll Bring - 5+ years of experience in building and deploying machine learning models in production environments. - Deep understanding of container orchestration and distributed systems architecture - Expertise in Kubernetes administration, including custom resource definitions, operators, and cluster management - Experience developing APIs and managing distributed systems for both batch and real-time workloads - Excellent communication skills, with the ability to interface between research and product engineering Ideally, You Have - Expertise with model serving frameworks such as NVIDIA Triton Server, VLLM, TRT-LLM and so on. - Expertise with ML toolchains such as PyTorch, Tensorflow or distributed training and inference libraries. - Familiarity with GPU cluster management and CUDA optimization - Knowledge of infrastructure as code (Terraform, Ansible) and GitOps practices - Experience with container registries, image optimization, and multi-stage builds for ML workloads - Experience orchestrating across ASR models or LLM models for building various GenAI applications WHY WORK AT ABRIDGE? At Abridge, we’re transforming healthcare delivery experiences with generative AI, enabling clinicians and patients to connect in deeper, more meaningful ways. Our mission is clear: to power deeper understanding in healthcare. We’re driving real, lasting change, with millions of medical conversations processed each month. Joining Abridge means stepping into a fast-paced, high-growth startup where your contributions truly make a difference. Our culture requires extreme ownership—every employee has the ability to (and is expected to) make an impact on our customers and our business. Beyond individual impact, you will have the opportunity to work alongside a team of curious, high-achieving people in a supportive environment where success is shared, growth is constant, and feedback fuels progress. At Abridge, it’s not just what we do—it’s how we do it. Every decision is rooted in empathy, always prioritizing the needs of clinicians and patients. We’re committed to supporting your growth, both professionally and personally. Whether it's flexible work hours, an inclusive culture, or ongoing learning opportunities, we are here to help you thrive and do the best work of your life. If you are ready to make a meaningful impact alongside passionate people who care deeply about what they do, Abridge is the place for you. HOW WE TAKE CARE OF ABRIDGERS: - Generous Time Off: 14 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees - Comprehensive Health Plans: Medical, Dental, and Vision coverage for all full-time employees and their families. - Generous HSA Contribution: If you choose a High Deductible Health Plan, Abridge makes monthly contributions to your HSA. - Paid Parental Leave: Generous paid parental leave for all full-time employees. - Family Forming Benefits: Resources and financial support to help you build your family. - 401(k) Matching: Contribution matching to help invest in your future. - Personal Device Allowance: Tax free funds for personal device usage. - Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits. - Lifestyle Wallet: Monthly contributions for fitness, professional development, coworking, and more. - Mental Health Support: Dedicated access to therapy and coaching to help you reach your goals. - Sabbatical Leave: Paid Sabbatical Leave after 5 years of employment. - Compensation and Equity: Competitive compensation and equity grants for full time employees. - ... and much more! EQUAL OPPORTUNITY EMPLOYER Abridge is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability. We're committed to providing reasonable accommodations throughout the interview process. Once you submit your application, we'll follow up with details on how to request an accommodation for interviewing, completing any assessments, or otherwise participating in the selection process. STAYING SAFE - PROTECT YOURSELF FROM RECRUITMENT FRAUD We are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com http://abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article https://consumer.ftc.gov/consumer-alerts/2023/05/scammers-are-hijacking-job-ads-heres-how-spot-fakes. Please exercise caution and cease communications if something feels suspicious about your interactions.
Member of Product Staff, Foundation Models & Post-...
ABOUT ABRIDGE Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients. Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems. We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh. THE ROLE Abridge was founded to power deeper understanding in healthcare. Our ambient AI platform transforms clinical conversations into high-quality documentation and is expanding into clinical workflows such as coding, clinical decision support, and care navigation. The quality of these products depends on the models beneath them - and on our ability to improve those models using signals that only Abridge can access at scale. We are hiring a Product Lead to partner with ML Science on Abridge's foundation-model and post-training work. You will help turn our proprietary corpus of clinical conversations, clinician edits, EHR context, and downstream actions into durable model capabilities. You will own the product strategy that connects research bets to product outcomes: where an in-house model can create meaningful advantage, which capabilities and workloads to prioritize, what evidence is required to scale an approach, and how a successful model moves from experiment to production. This is not a traditional feature-PM role, and it is not a research-program-manager role. You will operate at the intersection of model science, platform strategy, and clinical product delivery. You will need enough technical depth to challenge assumptions and make consequential tradeoffs with scientists and engineers, while keeping the work anchored in clinician value, patient safety, and business impact. You must be located in San Francisco for this opportunity or willing to relocate to San Francisco. WHAT YOU'LL DO - Set the product strategy for Abridge's model family. Translate company and product priorities into a coherent portfolio of models, with explicit choices across capability, quality, latency, cost, safety, and controllability. - Own the path from research to product impact. Define the hypotheses, milestones, decision gates, and success metrics that move models from experiments into shadow mode and production. - Turn proprietary data into a product advantage. Shape how Abridge uses de-identified conversations, final notes, clinician edits, EHR context, and care actions as training and feedback signals, in partnership with Data, Privacy, Security, and Clinical teams. - Make model investment decisions legible. Build clear frameworks for when to use a frontier model, an open model, a prompted workflow, or an Abridge-trained model; quantify expected quality, serving-cost, latency, control, and strategic benefits. - Partner with Evals on promotion criteria. Define the product-relevant capabilities and failure modes that matter for each model use case, while the Evals team owns shared measurement infrastructure and neutral evaluation standards. - Create a tight learning loop with product teams. Convert production failures, clinician feedback, edit behavior, and emerging product needs into training priorities; make model improvements visible in user and business outcomes. - Drive cross-functional execution. Align ML Science, ML Engineering, Product Engineering, Clinical Science, Data, Evals, and product pods around priorities, interfaces, ownership, and delivery - often without formal authority. - Communicate the strategy. Make complex research choices understandable to executives and product teams, clearly separating demonstrated results, working hypotheses, and long-term bets. WHAT YOU BRING - 7+ years of product-management or closely related experience, including substantial ownership of ML-powered products, model platforms, or AI infrastructure. - A track record of turning ambiguous technical capabilities into shipped products and measurable user or business outcomes. - Strong working knowledge of the modern model-development lifecycle, including data strategy, fine-tuning and preference optimization, evaluation, inference, experimentation, and production monitoring. - The technical judgment to reason with ML scientists and engineers about training objectives, reward design, data quality, model selection, scaling, latency, and serving cost - without pretending to be the scientist in the room. - Strong product judgment about where proprietary models create durable differentiation versus where external models or conventional systems are the better choice. - Experience creating clarity across multiple teams: defining decision rights, sequencing dependencies, resolving disagreement, and maintaining speed in a high-ambiguity environment. - A high bar for evidence, safety, and trust. You know that aggregate model scores can hide consequential failures and that clinical AI requires explicit escalation, abstention, and human-review paths. - Excellent written and verbal communication, including the ability to explain a technical strategy to both research teams and company leadership. BONUS POINTS IF... - You have directly worked on LLM post-training, reinforcement learning, preference optimization, distillation, model routing, or domain-adaptive pretraining. - You have managed a portfolio spanning frontier APIs, open-source models, and in-house models. - You have experience with human-feedback or expert-annotation systems, especially where feedback is sparse, subjective, or expensive. - You have shipped AI in healthcare, life sciences, or another safety-critical and regulated domain. - You have worked on products involving clinical documentation, medical reasoning, agentic workflows, or longitudinal context. - You have partnered with research teams on high-compute bets where staged evidence was required before scaling investment. WHY WORK AT ABRIDGE? At Abridge, we’re transforming healthcare delivery experiences with generative AI, enabling clinicians and patients to connect in deeper, more meaningful ways. Our mission is clear: to power deeper understanding in healthcare. We’re driving real, lasting change, with millions of medical conversations processed each month. Joining Abridge means stepping into a fast-paced, high-growth startup where your contributions truly make a difference. Our culture requires extreme ownership—every employee has the ability to (and is expected to) make an impact on our customers and our business. Beyond individual impact, you will have the opportunity to work alongside a team of curious, high-achieving people in a supportive environment where success is shared, growth is constant, and feedback fuels progress. At Abridge, it’s not just what we do—it’s how we do it. Every decision is rooted in empathy, always prioritizing the needs of clinicians and patients. We’re committed to supporting your growth, both professionally and personally. Whether it's flexible work hours, an inclusive culture, or ongoing learning opportunities, we are here to help you thrive and do the best work of your life. If you are ready to make a meaningful impact alongside passionate people who care deeply about what they do, Abridge is the place for you. HOW WE TAKE CARE OF ABRIDGERS: - Generous Time Off: 14 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees - Comprehensive Health Plans: Medical, Dental, and Vision coverage for all full-time employees and their families. - Generous HSA Contribution: If you choose a High Deductible Health Plan, Abridge makes monthly contributions to your HSA. - Paid Parental Leave: Generous paid parental leave for all full-time employees. - Family Forming Benefits: Resources and financial support to help you build your family. - 401(k) Matching: Contribution matching to help invest in your future. - Personal Device Allowance: Tax free funds for personal device usage. - Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits. - Lifestyle Wallet: Monthly contributions for fitness, professional development, coworking, and more. - Mental Health Support: Dedicated access to therapy and coaching to help you reach your goals. - Sabbatical Leave: Paid Sabbatical Leave after 5 years of employment. - Compensation and Equity: Competitive compensation and equity grants for full time employees. - ... and much more! EQUAL OPPORTUNITY EMPLOYER Abridge is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability. We're committed to providing reasonable accommodations throughout the interview process. Once you submit your application, we'll follow up with details on how to request an accommodation for interviewing, completing any assessments, or otherwise participating in the selection process. STAYING SAFE - PROTECT YOURSELF FROM RECRUITMENT FRAUD We are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com http://abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article https://consumer.ftc.gov/consumer-alerts/2023/05/scammers-are-hijacking-job-ads-heres-how-spot-fakes. Please exercise caution and cease communications if something feels suspicious about your interactions.
Member of Product Staff, AI/ML (Evals)
ABOUT ABRIDGE Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients. Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems. We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh. The Role Abridge's has multiple products - the core product is ambient clinical notes, but we’ve expanded surfaces: billing, clinical decision support, orders, nursing, etc. Measuring quality reliably and iterating fast without breaking clinician trust sit at the core of every single product’s success. The Evals team owns the tooling, templates, and consultation that product teams use across the eval lifecycle: curate, build, iterate, and deploy. This builds the strategy, platform, and process that enables evals to be fast, trustworthy, consistent, and a true moat. We are hiring a Product Manager to lead this platform. You will work with 10+ teams and partner closely with engineering, ML, data science, and clinician science. You will help set the standard for how Abridge measures AI quality, decide what the platform builds, and make evaluating a new model cheap and routine as frontier models ship frequently. What You'll Do - Drive product strategy and execution for the evals platform. Own the roadmap across the eval lifecycle and own outcomes against it. - Build the shared measurement infrastructure. Help build the systems that let any pod define quality, run experiments, compare models, and watch production. Own the standards for LLM judges, rule-based evaluators, human annotation, and online monitoring, and be clear about where each belongs. - Make model selection fast and routine. Give teams a repeatable way to evaluate a new frontier model within days of release, and a defensible framework for when a post-trained specialty model is worth it over a prompted frontier one. Keep the eval system model-agnostic so it stays a neutral referee. - Own the operating model across pods. Define the eval gates from early build through GA and steady-state monitoring, which gates are hard versus advisory, and who owns non-negotiable floors like critical-error rates. Land this across pods you don't own, without formal authority. - Cross functional execution. Work with Data Engineering on the de-identification pipeline, Data Science on bootstrapping judge quality with less human annotation, Clinical Science on flagged production cases, and the agent platform team as workflows go agentic. - Operate with a high bar for quality, speed, and accountability. What You Bring - 5+ years of product management experience with significant ownership of ML powered products or platform systems. - Deep understanding of how to measure and improve model quality, including evaluation frameworks, annotation pipelines, and benchmark design. - Strong technical fluency across ML, data pipelines, and distributed systems. - Experience working closely with ML researchers and engineers to drive impact in production. - Ability to balance long term architectural investments with near term quality improvements. - Strong communication skills and the ability to translate complex technical concepts into clear decisions and narratives. - A track record of delivering high quality products in domains where accuracy, reliability, and trust are paramount. Bonus Points If… - You have experience building evaluation platforms, ML observability systems, or quality measurement pipelines. - You have worked in clinical, healthcare, or regulated environments with a high bar for accuracy and compliance. - You have worked on specialty specific or domain specific model adaptations. - You have worked on personalization systems, context ingestion frameworks, or ambient intelligence products. - You have experience shipping large scale ML products with human in the loop workflows. WHY WORK AT ABRIDGE? At Abridge, we’re transforming healthcare delivery experiences with generative AI, enabling clinicians and patients to connect in deeper, more meaningful ways. Our mission is clear: to power deeper understanding in healthcare. We’re driving real, lasting change, with millions of medical conversations processed each month. Joining Abridge means stepping into a fast-paced, high-growth startup where your contributions truly make a difference. Our culture requires extreme ownership—every employee has the ability to (and is expected to) make an impact on our customers and our business. Beyond individual impact, you will have the opportunity to work alongside a team of curious, high-achieving people in a supportive environment where success is shared, growth is constant, and feedback fuels progress. At Abridge, it’s not just what we do—it’s how we do it. Every decision is rooted in empathy, always prioritizing the needs of clinicians and patients. We’re committed to supporting your growth, both professionally and personally. Whether it's flexible work hours, an inclusive culture, or ongoing learning opportunities, we are here to help you thrive and do the best work of your life. If you are ready to make a meaningful impact alongside passionate people who care deeply about what they do, Abridge is the place for you. HOW WE TAKE CARE OF ABRIDGERS: - Generous Time Off: 14 paid holidays, flexible PTO for salaried employees, and accrued time off for hourly employees - Comprehensive Health Plans: Medical, Dental, and Vision coverage for all full-time employees and their families. - Generous HSA Contribution: If you choose a High Deductible Health Plan, Abridge makes monthly contributions to your HSA. - Paid Parental Leave: Generous paid parental leave for all full-time employees. - Family Forming Benefits: Resources and financial support to help you build your family. - 401(k) Matching: Contribution matching to help invest in your future. - Personal Device Allowance: Tax free funds for personal device usage. - Pre-tax Benefits: Access to Flexible Spending Accounts (FSA) and Commuter Benefits. - Lifestyle Wallet: Monthly contributions for fitness, professional development, coworking, and more. - Mental Health Support: Dedicated access to therapy and coaching to help you reach your goals. - Sabbatical Leave: Paid Sabbatical Leave after 5 years of employment. - Compensation and Equity: Competitive compensation and equity grants for full time employees. - ... and much more! EQUAL OPPORTUNITY EMPLOYER Abridge is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability. We're committed to providing reasonable accommodations throughout the interview process. Once you submit your application, we'll follow up with details on how to request an accommodation for interviewing, completing any assessments, or otherwise participating in the selection process. STAYING SAFE - PROTECT YOURSELF FROM RECRUITMENT FRAUD We are aware of individuals and entities fraudulently representing themselves as Abridge recruiters and/or hiring managers. Abridge will never ask for financial information or payment, or for personal information such as bank account number or social security number during the job application or interview process. Any emails from the Abridge recruiting team will come from an @abridge.com http://abridge.com email address. You can learn more about how to protect yourself from these types of fraud by referring to this article https://consumer.ftc.gov/consumer-alerts/2023/05/scammers-are-hijacking-job-ads-heres-how-spot-fakes. Please exercise caution and cease communications if something feels suspicious about your interactions.
Research Engineer / Scientist (Robot Learning)
ABOUT WORLD LABS World Labs https://www.worldlabs.ai/ is a frontier AI research and product company advancing spatial intelligence, the next frontier beyond large language models. Co-founded by Dr. Fei-Fei Li https://profiles.stanford.edu/fei-fei-li, Justin Johnson https://www.linkedin.com/in/justin-johnson-41b43664/ and Ben Mildenhall https://www.linkedin.com/in/ben-mildenhall-86b4739b/, the company is pioneering world models that perceive, generate, reason, and interact with virtual and physical worlds. The company’s flagship product, Marble https://marble.worldlabs.ai/, transforms text, images, and video into fully navigable 3D worlds, unlocking applications across gaming, film, architecture, robotics, and immersive digital experiences. Backed by leading investors and with over $1B raised, World Labs is assembling a world-class team at the intersection of AI research and real-world deployment. ROLE OVERVIEW We’re looking for strong Robot Learning Engineer/Scientist to develop and advance state-of-the-art methods for developing robot policies. This role is focused on training end-to-end policies with an emphasis on sim-to-real transfer, robust performance, scalable training and inference pipelines. This is a hands-on, research-driven role for someone working at the intersection of robotics and machine learning. You’ll collaborate closely with research scientists, ML engineers and system teams to translate robotic policy and its stack into production-ready systems. WHAT YOU WILL DO - Design and implement modern robot learning systems, including imitation learning, reinforcement learning for manipulation. - Research, prototype, and productionize robotic policies with a focus on speed, precision and scalability. - Develop and improve training pipelines for sim-to-real transfer, including domain randomization, system identification, real-sim alignment. - Collaborate with simulation and infrastructure teams to minimize sim-to-real gap and ensure learning methods integrate cleanly with real-robot deployment stacks. - Build end-to-end training and evaluation workflows for robot policies, from large-scale data generation to scale up training and evaluation. - Optimize policy performance across the stack, including training speed, inference latency, data generation efficiency to support large scale production constraints. - Diagnose failure modes in simulation and real-world rollouts, design principled solutions to improve robustness, efficiency and generalization. - Contribute to technical direction by proposing new research ideas, mentoring teammates, and helping set best practices for robot learning across the organization. KEY QUALIFICATIONS - PhD in robotics, machine learning, computer science, or a closely related field is strongly preferred. - 6+ years of experience working on manipulation, locomotion, robot policy training, or related areas. - Strong foundation in robotics, neural network designs, sim-real transfer. - Deep experience with robot policy designs (e.g., VLA, WAM, diffusion). - Proficiency in Python and/or C++, with hands-on experience building research or production robotic systems. - Experience with deep learning frameworks (e.g., PyTorch) and low-level robotic controller. - Proven ability to work in ambiguous, fast-moving environments and drive projects from concept through deployment. - A strong sense of ownership and engineering rigor: you care deeply about correctness, stability, and measurable improvements. - Enjoy collaborating with a small, high-caliber team and raising the technical bar through thoughtful design, experimentation, and code quality. WHO YOU ARE - Fearless Innovator: We need people who thrive on challenges and aren't afraid to tackle the impossible. - Resilient Builder: Impacting Large World Models isn't a sprint; it's a marathon with hurdles. We're looking for builders who can weather the storms of groundbreaking research and come out stronger. - Mission-Driven Mindset: Everything we do is in service of creating the best spatially intelligent AI systems, and using them to empower people. - Collaborative Spirit: We're building something bigger than any one person. We need team players who can harness the power of collective intelligence. We're hiring the brightest minds from around the globe to bring diverse perspectives to our cutting-edge work. If you're ready to work on technology that will reshape how machines perceive and interact with the world - then World Labs is your launchpad. Join us, and let's make history together. Logistics: Location: This role is based in San Francisco, California; we work in-person in the office 5 days a week. Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. Benefits: We offer generous health, dental, vision and 401K benefits, flexible PTO, paid parental leave, and relocation support as needed. Equal Employment Opportunity World Labs is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, genetic information, veteran status, or any other characteristic protected under applicable law. We welcome all qualified applicants and are committed to providing reasonable accommodations throughout the hiring process upon request.
Staff Technical Lead Manager, Simulation, Machine...
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 team at Waymo develops a rich understanding of Waymo Driver's behavior. With over 1 million driverless miles per week, it is critical that Waymo can understand and assess the behavior of all its vehicles - both in the field and in simulation - with automated algorithms. The learned metrics team is a strategic bet to use machine learning to ensure we can scale to meet Waymo's goals. We collaborate across teams to bring ML to production systems and build what is Waymo's reward function. We build and operate large-scale machine learning and data systems, simulation workflows, and insight tools. We combine expert human judgements and advanced machine learning models to deliver training and evaluation data for the Waymo driver. We are looking for researchers and software engineers who are passionate about developing production grade machine learning systems for our autonomous vehicles and have an incessant drive to improve the performance of our technology stack. This role follows a hybrid work schedule and reports to an Engineering Manager. You will: - Lead a top-tier applied ML team focused on building ML models for AV behavior understanding and evaluation, using deep learning and Gen AI. - 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 - 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 - 5+ years of experience leading engineering teams of 5-15 people - 7+ years of experience with hands-on experience in machine learning projects - 7+ years of hands-on experience building and deploying machine learning products in production environments - Experience in at least one of: Foundational Models, VLM, Deep Learning - Strong coding experience in C++ and/or Python. - 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. - 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
Staff Machine Learning Engineer, Mapping
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 Waymo Mapping team's goal is to build a high resolution map of the world to support safe autonomous driving. We work on creating the map using a combination of automatic and manual techniques and build the infrastructure to store, process, and distribute the map. Our team collaborates with several other Waymo teams that consume map data. In this hybrid role, you will report to an Engineering Manager In this role, you will: - Design, train, and deploy machine learning models to automate the creation of Waymo's HD maps, unlocking scale for the Waymo Driver. - Apply and advance state-of-the-art ML techniques, including Vision-Language Models (VLMs) and other Generative AI approaches, to pioneer new solutions in mapping automation. - Own the complete model development lifecycle, from data mining and processing to model training, evaluation, validation, and productionization. - Collaborate closely with partner ML teams, such as Waymo Perception and Waymo AI Foundations, to adapt cutting-edge research into scalable, reliable, and production-grade solutions. You have: - 8+ years of hands-on experience in Machine Learning, with a strong focus on computer vision and/or deep learning. - Proficiency in at least one major deep learning framework (e.g., TensorFlow, PyTorch, JAX). - Demonstrated experience owning problems end-to-end and working across various parts of the systems stack to deliver results. - B.S. in Computer Science, a similar technical field, or equivalent practical experience. It's a Bonus if you have: - M.S. or Ph.D. degree in Computer Science or a related discipline. - Familiarity with foundation models and techniques for model adaptation (e.g., few-shot learning, transfer learning, domain adaptation). - A track record of publications in top-tier ML/CV conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV). - Experience with C++. - Direct experience with mapping or GIS is a bonus but not required. 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
Senior/Staff ML Engineer, 3D/4D World Modeling, Si...
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 Simulator Team at Waymo builds state-of-the-art simulations of realistic environments for testing, training, and validation of the Waymo Driver. Our team is a diverse, and collaborative group of machine learning (ML) engineers, software engineers, and ML research engineers. We develop industry-leading simulation solutions using advanced generative and reconstructive ML algorithms, to model the real world, encompassing realistic agents, roads, traffic systems, weather, and the full sensor suite (Camera, Lidar, Radar). To accelerate the fidelity, scalability, controllability, and richness of our simulations, we are pushing the frontiers of 3D world modeling. We leverage state-of-the-art ML technologies trained on large-scale datasets to create dynamic and semantically rich virtual worlds, directly impacting the development and validation of the Waymo Driver. In this role, you will report to a Senior Staff Engineering Manager. You will: - Lead the design, development and deployment of cutting-edge 4D world models and generative systems for ultra-realistic and controllable sensor and semantics generation for simulation use cases at waymo. - Architect and implement scalable and robust ML pipelines for training, evaluating, and deploying large-scale generative models into our simulation infrastructure, including techniques like model distillation and quantization. - Build and scale production-ready video generation techniques (e.g., Diffusion, Flow Matching) to create dynamic and interactive simulation environments. - Apply Vision Language Models (VLMs) to enhance the semantic understanding and controllability of our world simulation products. - Partner with world class research teams across Waymo and Alphabet to leverage State-of-The-Art research in 4D world modeling and generative AI into robust, production-ready solutions. - Mentor and provide technical guidance to other engineers on the team. You have: - MS or PhD in Computer Science, Machine Learning, Robotics, or a related field. - 5+ years of experience in ML engineering and applied Deep Learning, with a strong portfolio of shipped products or publication record. - Proven experience in developing and training large-scale generative models for video generation (e.g., Diffusion models, Flow Matching) or Vision Language Models (VLMs) and their applications. - Deep expertise in 3D World Modeling or 3D computer vision. - Familiarity with 3D reconstruction and rendering techniques (e.g., 3D Gaussian Splatting). - Strong programming skills in Python and experience with ML frameworks such as Jax/Flax, PyTorch or Tensorflow. We prefer: - PhD and a strong track record of delivering impactful ML products in 3D generative models, world models, or video generation.. - Experience in simulating sensor data (Camera, Lidar, Radar) and/or semantic scenes. - Experience with autonomous systems, robotics, or autonomous vehicle simulation. - Experience in training and optimizing large scale models on GPU/TPU clusters for efficient serving. - Experience in C++ for production systems. LI-Hybrid 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
Research, Post-Training Evals
ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for a researcher to help develop reliable model evaluations for research signals. This role spans evaluation creation, usability, auditing, and efficiency. You’ll work closely with researchers and engineers across post-training and the broader research organization. Depending on your interests and experience, you may focus on one area or work across several of these problems. WHAT YOU’LL DO - Create internal evaluations and research signals for capabilities and behaviors important to model research and post-training. - Develop usability evaluations that measure whether models are genuinely useful in real research and product workflows, and partner with the data flywheel to turn evaluation insights into better data and training signals. - Improve evaluation robustness, including grader reliability, ambiguous ground truth, evaluator disagreement, false positives and negatives, and gaps between measured and intended behavior. - Build benchmark auditing methodologies that help researchers understand, trust, and appropriately use evaluation signals. - Develop specialized agentic evaluation environments and user simulators, including supporting harness development and studying cross-user, cross-harness and cross-environment generalization. - Develop evaluations for personalized preferences, biases, values, and other nuanced dimensions of model behavior in collaboration with post-training crafting. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Experience designing, building, or analyzing evaluations, benchmarks, datasets, graders, or other measurement systems. - Strong written and verbal communication skills, with the ability to collaborate effectively across research and engineering teams. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Experience with LLMs, post-training, reinforcement learning, or agentic systems. - Experience with evaluation auditing, human evaluations, LLM-judges, or open-ended task evaluation. - Experience with agentic evaluation, harnesses, long-horizon tasks, or RL environments. - Experience evaluating preferences, personalization, biases, values, or other nuanced model behaviors. - Track record of developing new evaluation methodologies or research signals that meaningfully influenced model development. - Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales. - Strong research judgment: clean ablations, honest baselines, and clear technical writing. - PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Research Engineer, Infrastructure, Inference
ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE We’re looking for an infrastructure research engineer to design, optimize, and scale the systems that power large AI models. Your work will make inference faster, more cost-effective, more reliable, and more reproducible to enable our teams to focus on advancing model capabilities rather than managing bottlenecks. Our focus is on performant and efficient model inference both to power real-world applications and to accelerate research. This role is responsible for the infrastructure that ensures every experiment, evaluation, and deployment runs smoothly at scale. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role. WHAT YOU’LL DO - Work alongside researchers and engineers to bring cutting-edge AI models into production. - Collaborate with research teams to enable high-performance inference for novel architectures. - Design and implement new techniques, tools, and architectures that improve performance, latency, throughput, and efficiency. - Optimize our codebase and compute fleet (e.g., GPUs) to fully utilize hardware FLOPs, bandwidth, and memory. - Extend orchestration frameworks (e.g., Kubernetes, Ray, SLURM) for distributed inference, evaluation, and large-batch serving. - Establish standards for reliability, observability, and reproducibility across the inference stack. - Publish and share learnings through internal documentation, open-source libraries, or technical reports that advance the field of scalable AI infrastructure. SKILLS AND QUALIFICATIONS Minimum qualifications: - Bachelor’s degree or equivalent experience in computer science, engineering, or similar. - Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures. - Experience with inference serving systems optimized for throughput and latency (e.g., SGLang, vLLM). - Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts. - A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships. - Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Experience training or supporting large-scale language models with hundreds of billions of parameters or more. - Understanding of distributed compute systems, GPU parallelism, and hardware-aware optimizations. - Contributions to open-source ML or systems infrastructure projects (e.g., SGLang, vLLM, PyTorch, Triton, DeepSpeed, XLA). - Track record of improving research productivity through infrastructure design or process improvements. LOGISTICS - Location: This role is based in San Francisco, California. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Staff Applied AI Engineer
🚀 About Us We’re Bolt.new by StackBlitz! We’re the team that brought you WebContainers, the first-of-its-kind technology that made it possible to run Node.js right inside your browser. That breakthrough kicked off our journey in 2019, and it’s what powers the blazing-fast online IDE used by over 1 million developers every month. But we didn’t stop there. We doubled down on everything we learned and built Bolt.new — the fastest way to go from idea to production without writing traditional code. It’s a next-gen, AI-powered app builder that helps you create, edit, and deploy full-stack web and mobile apps instantly, right in your browser. No installs. No setup. Just smart automation and instant dev environments that let you move at the speed of thought. We’re a fully remote team, globally distributed, deeply collaborative, and seriously passionate about building the future of software development. This is your chance to join a small team with a big vision. If you love shipping fast, solving real problems, and pushing the boundaries of what’s possible, we’d love to meet you. ✨ About This Opportunity As a Staff Engineer on the AI team, you'll lead the technical direction of the AI agents that turn natural language into production-ready applications. This means shaping how we work with LLMs to solve our hardest problems: maintaining context across large codebases, orchestrating multi-step workflows that feel intuitive, and handling everything from simple UI tweaks to complex architectural decisions. This isn't API integration work. You'll define the patterns and systems that govern how AI reasons about and generates full-stack applications, driving initiatives across multiple teams and influencing our broader AI strategy. Your work shapes the experience of millions of users building real products with Bolt every day. 🛠️ How You'll Contribute - Define AI Agent Architecture: Set technical direction for how agents manage context, orchestrate workflows, and scale. - Lead multi-model strategy: Build evals and selection criteria across providers (OpenAI, Anthropic, Google); partner with provider teams to test new capabilities. - Build tool-use & workflow foundations: Design safe, reliable interfaces for tool calling (search, queries, domain actions); evaluate frameworks (e.g., Vercel AI SDK, LangGraph) and set org best practices. - Drive cross-team execution: Align product/design/engineering, resolve tradeoffs, and mentor engineers to raise AI engineering standards. - Establish data & evaluation standards: Own dataset methodology and the eval harness; turn failure modes and conversation insights into measurable improvements. - Drive Research and Innovation: Experiment with prompting, context handling, and post-training; share learnings externally when appropriate. 💡 Qualifications - Deep LLM experience: Built and scaled production LLM systems; strong grasp of capabilities, limits, and emergent behavior. - Prompt engineering: Sets best practices and mentors across models and use cases. - Software engineering: Strong fundamentals; designs scalable systems and makes pragmatic architectural calls. - Strategic execution: Drives ambiguous, high-scope work end to end; influences across teams. - Systems thinking: Spots process/communication/technical debt and improves team velocity. - Model & agent literacy: Tracks coding-agent/LLM advances; understands model tradeoffs and the agent lifecycle. - Data-driven leadership: Builds data collection + eval harnesses; turns insights into measurable improvements. - Strong verbal and written English communication skills are required, as this role involves frequent collaboration with team members, stakeholders, and customers where English is the primary working language. 🎯 Bonus Points - Fine-tuning and alignment o f: LLMs (SFT, RLHF/RLAIF, DPO/ORPO) - Machine Learning Background: Understanding of ML fundamentals and experience with model evaluation metrics. - Open Source Contributions: Experience contributing to or maintaining open-source AI/ML projects. - Research Background: Experience reading and implementing techniques from AI/ML research papers. - External Presence: Experience speaking at conferences, publishing technical content, or representing an organization in industry forums. 📌 A Few Notes - You do not need a college degree to apply - You do not need to be located in the U.S. — we’re remote-friendly - You do not need to meet every qualification listed above
Research Scientist
Company Overview At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios without failing. We believe massive scale through data-driven machine learning is the key to unlocking these capabilities for the widespread deployment of robots within society. Our team consists of individuals with varying levels of experience and backgrounds, from new graduates to domain experts. Relevant industry experience is important, but ultimately less so than your demonstrated abilities and attitude. We are looking for passionate individuals who are eager to explore uncharted waters and contribute to our innovative projects. Position Overview We are looking for Research Scientists to lead the effort in developing the next generation of techniques to advance the capabilities of our robotic systems. We are looking for people with proven expertise in machine learning and/or robotics, who are passionate about pushing the boundaries of what is possible. You will collaborate with a team of talented researchers and engineers, and enhance our cohesive research strategy, driving ongoing innovation and technological advancements within the company. The ideal candidate will have a track record of discovering fundamental, scalable solutions, and a passion for seeing their research transform into impactful applications. Responsibilities - Develop new algorithms and methods for training general-purpose robot foundation models. - Conduct cutting edge research across multiple disciplines (Perception, Robotics, RL / IL, Machine Learning, etc.). - Work with large scale systems and large-scale model training. Preferred Qualifications - PhD degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience. - Multiple years of experience in conducting research. - Proficiency in Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc. - Publications at top-tier ML, robotics, or CV conferences (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, ECCV, ICCV). - Deep technical knowledge, and research experience in deep learning, reinforcement learning, robotics, or computer vision. - Deep understanding of state-of-the-art machine learning techniques and models. - Experience working with robot systems. - Experience working with large-scale model training and systems. Base Salary Range $100,000 - $300,000 USD
Member of Technical Staff, Machine Learning
ABOUT US Sieve is a multi-modal lab curating the world's highest-quality training datasets — spanning video, audio, images, text, and 3D. We combine exabyte-scale data infrastructure and novel multimodal understanding techniques that push the frontier of foundation models. Video alone makes up 80% of internet traffic, and across modalities, data has become the enabling medium powering creativity, communication, gaming, AR/VR, and robotics. Sieve exists to solve the biggest bottleneck in the growth of these applications: high-quality training data. We partner with top AI labs and did $XXM last quarter alone, as a team of ~30 people. We also raised our Series A from Tier 1 firms such as Matrix Partners https://matrix.vc/, Swift Ventures https://www.swift.vc/, Y Combinator https://www.ycombinator.com/, and AI Grant https://aigrant.com/. WHY NOW Sieve is one of the most capital-efficient teams in AI — roughly 30 people serving the world's leading AI labs across every major data modality. You'll join early, own problems end-to-end, and watch your work ship directly into the models defining the frontier. ABOUT THE ROLE As a Machine Learning Engineer at Sieve, you'll own the entire ML lifecycle — from understanding customer problems, to designing datasets, improving models, building evaluation systems, and shipping production pipelines that deliver measurable improvements in dataset quality. You'll work directly with frontier AI labs to understand difficult data problems, then build end-to-end systems that solve them. One week you might fine-tune a multimodal model to improve recall on a difficult edge case. The next you might engineer a VLM-based QA pipeline, design a new evaluation framework, or run a large-scale filtering pipeline on millions of hours of multimodal data. We're looking for engineers who enjoy owning problems end-to-end, from understanding customer requirements through shipping production ML systems that measurably improve dataset quality. WHAT YOU'LL DO - Own model quality for customer-facing video understanding problems - Fine-tune vision-language and multimodal foundation models for specialized tasks - Build automated evaluation and QA pipelines using frontier models like Gemini, GPT, Claude, and open-source VLMs - Design high-precision filtering, ranking, retrieval, and labeling systems over internet-scale video datasets - Create datasets, benchmarks, and evaluation frameworks that continuously improve model quality - Develop production ML pipelines spanning preprocessing, inference, post-processing, and quality validation - Work directly with frontier AI labs to translate ambiguous requirements into scalable ML systems - Ship improvements quickly, measure results, and iterate based on real-world performance REQUIREMENTS - Strong Python engineer with experience building production ML systems - Experience training, fine-tuning, or deploying modern deep learning models - Comfortable working with PyTorch and modern foundation models - Excellent intuition for evaluation, dataset quality, precision/recall tradeoffs, and edge cases - Enjoys rapidly prototyping with new AI models and APIs - Comfortable owning projects from customer problem to internal pipelines to deployed solution - Strong communicator who enjoys working directly with customers and cross-functional teams - Excited by video, multimodal AI, and frontier foundation models - In-person at our SF HQ *all roles at Sieve require you to be onsite in San Francisco 5 days per week
Machine Learning Scientist, Reinforcement Learning
Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date. We're looking for a motivated and creative Machine Learning (ML) Scientist to drive research into reinforcement learning for biomolecular design. This position offers an opportunity to work at the forefront of generative modeling research across language processing, representation learning, and protein engineering. You should be a self-directed researcher who has the ability to rapidly prototype and evaluate new models and algorithms in the biomolecular domain. As an early employee, you will proactively shape the direction of our machine learning efforts and collaborate across diverse teams of computational and experimental scientists. Responsibilities - Design and develop state-of-the-art online and offline reinforcement learning algorithms for protein design - Collaborate across the machine learning and protein design teams to adapt and improve reinforcement learning techniques from other domains to protein design - Architect, implement, and optimize core infrastructure to support the post-training of protein language models - Curate relevant datasets and design tasks for rigorous evaluation of generative models - Implement, analyze, and interpret multiple computational approaches and present results to colleagues in regular update meetings - Work within a collaborative, fast-paced, interdisciplinary team across biology and machine learning to help shape the scientific and strategic vision of the company Qualifications - PhD (or equivalent industry experience) in Computer Science, Machine Learning, Natural Language Processing, Applied Math, Computational Biology, Statistics, or a related field - Experience with conceiving of, implementing, and evaluating novel machine learning and reinforcement learning techniques - Publications at major machine learning conferences (NeurIPS, ICML, ICLR) or scientific journals (Nature, Science, Nature Biotech, Nature Methods, PNAS) - Experience with modern deep learning frameworks such as Pytorch or Jax Preferences - Familiarity with foundational biology of proteins and nucleic acids - Experience developing machine learning models for proteins (language models, structure prediction, design) - Experience with cloud compute platforms (GCP, AWS, Azure, OCI) - Previous experience in data extraction and curation from bioinformatics data sources - Familiarity with wet lab experimental assays and associated limitations What We Offer - High-growth opportunity with meaningful impact on the future of protein design - Competitive compensation package with equity participation - 401(k) with a strong employer match - Comprehensive benefits including health/dental/vision insurance - Generous PTO policy and commitment to work-life balance - Professional development opportunities in a cutting-edge field at the intersection of AI and biology Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law. Employment Eligibility Verification Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire. Hiring Salary Range $212,000 - $339,000 USD NOTICE TO STAFFING AND SEARCH FIRMS: Profluent does not accept unsolicited candidate submissions. We ask that agencies refrain from sending resumes to our careers inbox, hiring managers, or any Profluent employee. Any resume submitted without a signed agreement and an active search request from our Talent team will be treated as the property of Profluent, and no placement fee will be owed.
Product ML Engineer
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. ROLE SUMMARY We're looking for a Product ML Engineer to join one of our product experience teams — SMB, Scaler or Enterprise — and own the ML side of short, high-impact projects. You'll work on focused ML problems with a clear product outcome, sometimes fine-tuning one of our models, sometimes integrating the best externally available model, but always choosing the fastest and most effective path to customer value. You'll bring the technical depth of an Applied Scientist while operating with the pace and pragmatism of a product engineer. You'll own the journey from framing the problem through to shipping, measuring and iterating on the feature in production. This is not a research role. Your time horizon is weeks rather than quarters, and success is measured by what reaches users and the impact it creates. ABOUT THE ROLE - Embed directly within a product team. Work closely with the SMB, Scaler or Enterprise team on focused ML problems tied to clear customer and business outcomes. - Own ML projects end to end. Frame the problem, choose the approach, execute the plan, ship the feature and monitor its performance in production. - Make the build vs. buy decision. Determine when to fine-tune or build a model yourself versus integrating an external model, API or existing library. - Move from idea to production quickly. Work on projects scoped in weeks rather than quarters, prioritising rapid iteration and customer impact. - Work closely with the ML team. Leverage shared infrastructure, evaluation tooling and expertise while remaining embedded in the product team's priorities and cadence. - Stay close to users. Get rapid feedback from customers, monitor how people use what you've shipped and use those insights to drive the next iteration. - Balance technical depth with pragmatism. Know when deeper modelling work is necessary and when an existing solution is already good enough to ship. - Write production code. Work directly in the product team's codebase rather than operating primarily through research notebooks and experiments. - Own quality in production. Build sound evaluations, monitor performance and investigate failures once the feature is live. ABOUT YOU - You've shipped ML into production. You have 2+ years of hands-on experience building and shipping ML-powered products, with strong experience in PyTorch or equivalent frameworks such as TensorFlow or JAX. - You have applied-science depth. You can fine-tune vision or diffusion models, design meaningful evaluations and debug training runs when necessary. - You're a pragmatic builder. You care about solving the customer's problem rather than proving that your own model is the best approach. You'll happily use an external model, API or existing library when that's the fastest route to value. - You're product-minded. You measure success through shipped features, user behaviour and business outcomes rather than papers or benchmarks alone. - You're comfortable in production codebases. You can move between experimentation and production engineering, writing maintainable code that lives inside a real product. - You have strong ownership. You're comfortable taking an ambiguous ML problem, deciding what matters, choosing an approach and driving it through to production. - You iterate quickly. You enjoy short feedback loops and would rather ship something useful, learn from users and improve it than spend months optimising something that hasn't reached production. - You're curious and collaborative. You can both explain your expertise and learn from the deep technical expertise around you. You value knowledge sharing and humility. - You're fluent in English. French isn't required. Nice to have: experience shipping generative image models or diffusion models into production; experience evaluating third-party AI APIs against internal alternatives; experience working in a fast-growing startup or directly within a product team. BENEFITS - Competitive salary + meaningful equity - Hybrid working from our Paris headquarters - Visa sponsorship and relocation support up to €10,000 - MacBook Pro and full home office setup - 30 days annual leave - Private healthcare - Regular company off-sites and team retreats - International English-speaking team - Language lessons in English and French HIRING PROCESS 1. Screening with an ML Researcher / Engineer 2. Home technical assignment and review with the team 3. Culture fit interview and meet the team 4. Reference check and offer Support: We value diversity and aim to create an inclusive experience for all applicants. Please let us know if there's anything we can do to make the process more accessible for you. 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.
Senior Deep Learning Engineer, Accuracy Evaluation
We are seeking senior engineers to pioneer new methodologies for accurately assessing the performance and capabilities of ground-breaking deep learning models, including LLMs, RAG, agents, and vision models. You will collaborate across the organization to bring the latest flagship models from our community and partners to life. This role offers an outstanding opportunity to craft the future of AI at a fast-growing company at the forefront of the AI revolution. Join our team of world-class software engineers and partners to deliver the most advanced models with lightning-fast inference. You'll work on the most powerful, enterprise-grade GPU clusters capable of hundreds of PetaFLOPS and gain early access to unreleased hardware, making a direct impact on NVIDIA's roadmap and the broader AI landscape! What you’ll be doing: Design and build decision-grade evaluation environments for NVIDIA's frontier models spanning reasoning, multimodal, long-context, and agentic systems, producing auditable accuracy signals that gate every major model release. Research and develop novel evaluation methodologies for emerging model families and capability domains (low-precision numerics, multi-turn agentic tasks, code generation) where established benchmarks don't yet exist or don't generalize. Build and operate the evaluation infrastructure and pipelines including benchmark environments, regression CI systems, and statistical analysis tooling, used by model, product, and applied research teams across NVIDIA. Partner with model research, training, and customer teams to translate evaluation signals into concrete decisions: release go/no-go, training iteration direction, and competitive positioning against external frontier models. What we need to see: BS, MS, or PhD in Computer Science, Machine Learning, Statistics, or a related field 6+ years of hands-on experience with LLMs, designing and running evaluations for large language models or multimodal AI systems, including experience with agentic, multi-turn, or reasoning-heavy settings. Strong statistical foundations: experimental design, significance testing, regression analysis, and the ability to distinguish signal from noise in benchmark results at scale. Proven experience building evaluation infrastructure (pipelines, benchmark harnesses, reproducible CI systems) not just consuming existing benchmarks. Clear, precise communicator who can translate quantitative evaluation results into decisions for researchers, product teams, and senior leadership. Ways to stand out from the crowd: Deep familiarity with open-source evaluation frameworks Experience designing evaluations for agentic systems: tool use, multi-turn reasoning, environment-based benchmarks (SWE-bench, GAIA, WebArena-style), or interactive evaluation settings. Track record of publishing or contributing to evaluation research, new benchmark design, methodology papers, or reproducibility analyses that shaped how the field measures model capability. Experience measuring model accuracy under low-precision inference (FP8, INT4, quantization-aware settings) and understanding how calibration and sparsity interact with benchmark results. Comfort running large-scale workloads on HPC/Slurm clusters, including reproducible experiment management (MLflow, W&B) and compute cost optimization across hundreds of benchmark runs. NVIDIA is committed to fostering a diverse 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. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 375,000 PLN - 650,000 PLN.
Senior AI Systems Engineer
NVIDIA is looking for a talented AI Solutions Engineer to join our innovative AI team. The team develops advanced AI platforms and applications including agentic workflows, Retrieval-Augmented Generation (RAG) systems, Large Language Model (LLM) based solutions, AI agents, MCPs, and more. You will have the opportunity to shape how AI transforms our products and internal processes worldwide. Your responsibilities will include implementing state-of-the-art AI technologies, developing all aspects of the project – AI, code, data preparation, evaluation, and deployment. We are looking for a motivated teammate who thrives on solving complex problems with AI and continuously explores emerging techniques in this rapidly evolving field. What you'll be doing: Design and implement Agentic platforms and workflows to simplify the agentic experience across the company. End-to-end solutions Implementation to increase the organization quality and productivity. Develop AI solutions that integrate seamlessly with existing products and workflows Build evaluation frameworks to measure and improve AI system performance Collaborate with cross-functional teams to identify and implement AI opportunities Collect and prepare data from multiple large scale sources for AI training and inference What we need to see: B.Sc. (or equivalent experience) in Computer Science, AI, Machine Learning or related field 6+ years of experience in software development and building production-grade software systems. 1+ years of experience building LLM-based solutions, AI agents, and AI workflows. Proficiency in Python Strong understanding of modern AI concepts and practical applications Proficiency in Python Ways to stand out from the crowd: Strong understanding of modern Machine Learning domains, known algorithms, architectures and techniques Experience developing large scale software, including in a micro services architecture Experience with vector databases, embedding technologies and RAG systems Experience developing AI agent architectures and orchestration systems
Relational Foundation Model Engineer, Modern Data...
NVIDIA is redefining what is possible with AI, and the Relational Foundation Model team is helping lead that transformation. We are building a unified foundation model that can understand the structure, context, and relationships within relational databases and heterogeneous graphs—opening a new frontier for enterprise AI. As an engineer on this team, you will go beyond adapting existing models: you will design, build, and evaluate novel Transformer and graph neural network architectures that generalize across diverse data schemas. Your work will power meaningful applications, including recommendation systems, demand forecasting, fraud detection, and predictive maintenance. You will partner with world-class researchers and engineers across the full machine learning lifecycle, from architecture exploration and large-scale training to post-training optimization and high-performance inference. This is an opportunity to turn foundational research into production systems that influence how organizations derive intelligence from complex, connected data. NVIDIA foundation models are optimized for NVIDIA-accelerated infrastructure, providing a strong platform for translating advanced AI research into deployable systems. If you are energized by graph learning, relational reasoning, and building AI that moves beyond single-table benchmarks, we would love to hear from you What you’ll be doing: Collaborate with researchers/engineers to enhance our Transformer and GNN-based models to operate seamlessly over any relational schema and heterogeneous graph. Gain hands-on experience with high-impact use cases such as forecasting, entity matching, customer retention and fraud detection – all built on top of a single, extensible foundation model. Leverage your knowledge in ML and AI to tackle real challenges while contributing to scalable and adaptable solutions that push the boundaries of what’s possible. Work may span the full lifecycle of modern ML systems: from architecture design/training to post-training optimization and inference acceleration. You will contribute to our next generation of the Relational Foundation Model. What we need to see: MS or PhD in Machine Learning, Computer Science, or equivalent experience Proficiency in Python and deep learning frameworks, such as PyTorch At least 8 years of research experience in designing ML algorithm solutions Practical experience in using Predictive Models in Real World Applications Ways to stand out from the crowd: Familiarity with graph-based machine learning; publications at venues such as NeurIPS, ICLR, ICML, or similar NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, 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 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 21, 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.
Senior Software Engineer, Perception, Machine Lear...
Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role Our team is growing and we are looking for experienced machine learning researchers and engineers to join us. In this role, you will apply your expertise in machine learning to solve an array of challenges in Nuro's autonomy stack — detection with sensor fusion, tracking, fine grained classification, human intent understanding, to name a few. About the Work - You will be involved in all stages of problem solving, including initial proof of concept, model iteration, onboard deployment, and on-road performance monitoring and troubleshooting. - You will develop techniques and/or processes that allow us to train models that are able to utilize massive scale of data, but still keep the model onboard latency in check so that it is deployable. - You will have the opportunity to take a novel problem, assume the ownership, and just go deep on it. About You - Recent experience in applying state-of-the-art deep learning techniques to solving autonomous driving problems, such as modern object detection models, sensor fusion, and multiple task learning. - Strong ML fundamentals and has demonstrated experience building and expanding model architectures - Strong SWE fundamentals and has demonstrated SWE experience in both high and low-level languages (Python and C++) - Bonus: Experience in deploying models in real time environments. Optimizing architectures to explore the accuracy vs. compute tradeoff - Bonus: Experience working with productizing ML models and the infrastructure supporting model development (data, large scale training, eval, etc) At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected pay range is between $183,825.00 and $275,975.00/year for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package.
Senior Deep Learning Scientist, Multimodal Agentic...
NVIDIA is widely regarded as one of the technology industry’s most desirable employers. We lead the way in High-Performance Computing, Artificial Intelligence, and Visualization. Our core invention, the GPU, serves as the visual cortex of modern computers and powers our entire product suite. GPU deep learning ignited the modern AI era—the next great computing age—with the GPU acting as the brain for everything from robots and autonomous cars to conversational AI. Today, we are known globally as "the AI computing company." We are looking to grow our teams by bringing in the smartest people in the world. Join us at the forefront of technological advancement. NVIDIA is hiring Senior Deep Learning Scientists to advance our efforts in streaming and agentic multimodal AI. You will demonstrate foundational expertise in deep learning, reinforcement learning, and applied mathematics to help develop models capable of reasoning, planning, and acting across diverse modalities. This is a chance to define core algorithmic improvements for multimodal foundation models, scaling your ideas through our Nemotron Omni and VoiceChat platforms. You will work on high-impact, high-visibility large language models and multimodal AI products that improve the experience for millions of users. If you are creative and passionate about solving real-world agentic AI challenges, come join our Nemotron LLM team. For more details on Nemotron LLM, check https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/ What you’ll be doing: Apply fundamental and applied research to develop, train, fine-tune, and deploy large language models for agentic systems encompassing audio-visual reasoning, tool usage, and document understanding. Advance post-training and alignment methods including instruction tuning, preference optimization, and RLHF/RLVR/MOPD to improve multimodal agents for complex use cases. Research and develop agentic reasoning and grounded perception capabilities, focusing on planning, tool execution, and long-horizon task completion across digital and physical environments. Lead the collection, development, and benchmarking of multimodal datasets, ensuring high-quality evaluation of model accuracy, safety, and task completion success. What we need to see: Master’s degree (or equivalent experience) or PhD in Computer Science, AI, or Applied Math with 8+ years of relevant work experience. Excellent programming skills in Python with strong fundamentals in scalable model development and deep learning frameworks like PyTorch. Strong knowledge of ML/DL techniques and modern foundation model architectures, including Transformers and mixture-of-experts models. Foundational understanding of reinforcement learning algorithms and implementation, including MDPs, policies, and reward design. Hands-on experience in post-training multimodal models for omni-modality (audio-visual) reasoning, full-duplex voice chat, and human-AI interaction. Proven ability to manage model development life cycles, including dataset versioning, experiment tracking, and evaluation pipelines. Ways to stand out from the crowd: Strong record of publications in top-tier AI and machine learning venues such as NeurIPS, ICML, ICLR, or CVPR. Validated experience training and deploying multimodal foundation models using large-scale distributed infrastructure. Experience applying deep reinforcement learning techniques to train multimodal agents in complex simulation or gaming environments. Background in audio/speech AI, especially audio language models or audio generation. Background in building embodied AI systems that integrate multimodal perception with backend action-fulfillment and long-horizon planning. With highly competitive salaries and a comprehensive benefits package, NVIDIA is considered one of the industry’s most desirable employers. As you plan your future, see what we can offer you and your family at www.nvidiabenefits.com/ . If you are a creative and autonomous engineer with a genuine passion for state-of-the-art 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 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 6, 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.
PhD Data Scientist, Intern
Who we are About Stripe Stripe is a technology company focused on improving the conditions for economic growth and prosperity. We build programmable financial infrastructure, rethinking from first principles how financial services should work, to make it easier and cheaper for any business to start and scale. More than 10 million businesses build on Stripe, spanning the economic frontier—from solo founders to established enterprises—united by a practical focus on growth. The most ambitious companies in the world use Stripe as core infrastructure to grow faster. They process trillions of dollars a year on Stripe, equivalent to around 1.6% of global GDP. While economic growth makes everyone better off, open markets also enable greater variety. When any business can easily serve a global customer base, the quality and diversity of products in the world increase, and craft and creativity are unleashed into the smallest niches. Our own growth is wholly contingent on the success of the businesses building on Stripe. We therefore invest back into our technology at an unusual rate. We make upgrades to our products every single day to deliver compounding gains to our customers. We maintain some of the most reliable APIs on the internet. We build entirely new pieces of financial infrastructure to enable new ideas. And our significant advances in risk and fraud infrastructure over many years are making the internet economy safer and more accessible. Though people at Stripe don’t tend to take themselves seriously, Stripe is a fairly serious place: our customers are depending on us for their livelihoods. We admire ambition, intensity, curiosity, humility, and rigor. The most effective people become knowledgeable about many domains besides their own. Any company is an applied exercise in understanding some aspect of society or the market. In working with so many (especially the new and innovative ones), we think that Stripe is one of the very best places to learn about how the world works. About the team Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our Fraud, Losses, and Financial Crime Data Science team builds the models and data products that protect Stripe and its users from fraud, account takeover, and financial crime. We own the full fraud and loss modeling stack - from account takeover detection and card fraud classification to merchant-level loss estimation, unsupervised anomaly detection, and financial crime risk modeling. We partner with Fraud Engineering, Financial Crimes Engineering, and Risk Operations to bring these systems into production and ensure they have measurable impact on Stripe's financial integrity and user trust. What you’ll do As an intern at Stripe you’ll work on projects across our stack that directly impact the way millions of businesses operate. You’ll own problems end to end with the support of your manager and teammates. It’s an opportunity to work alongside some of the most creative and technically rigorous data analysts and scientists in the industry. You'll partner deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. You'll work closely with partners to extract insights from the rich and complex data at Stripe. You'll build metrics, scalable data pipelines, dashboards, and reports to inform and run the business. You'll deliver actionable business recommendations through analyses and data storytelling. Our internship program is competitive and the expectations are high. Responsibilities - Applying probability distributions, statistical inference, and hypothesis testing to quantify uncertainty and evaluate business outcomes - Using Python or R for data analysis, data processing, visualizations, statistical modeling, machine learning, predictive analytics, automation, and implementing causal inference and experimental analyses - Building, training, and evaluating predictive models across regression and classification tasks for bias-variance trade-offs and model selection - Modeling temporal dependencies, seasonality, and trend decomposition to generate and evaluate time-series predictions - Identifying structural patterns, clusters, and outliers in unlabeled data - Deploying models in production and adjusting model thresholds to improve performance - Designing, running, and analyzing complex experiments and leveraging causal inference designs - Using SQL and Spark to create, transform, and analyze large datasets - Learn quickly by asking great questions, finding how to work with your mentor and teammates effectively, and communicating the status of your work clearly - Present your work to the Data Science team, partner teams, and fellow interns. Who you are - Ambitious builder: You’re energized by building solutions without clear precedent and solving problems with far-reaching consequences. Successful Stripes are deeply curious, and prefer the joy of discovery to the comfort of certainty. - Rigorous thinker: You appreciate that things worth doing are rarely simple. You enjoy working on problems that have never been tackled before. - Adaptable problem solver: You adapt quickly and treat obstacles as opportunities. At Stripe we embrace kindness while encouraging Stripes to take measured risks and act boldly, even in the absence of consensus. Minimum requirements - Enrolled in a quantitative PhD program (e.g. Data Science, Statistics, Economics, Mathematics, etc.) with the expectation of graduating in December 2027 or spring/summer 2028 - Experience with SQL and a scientific computing language (such as Python, R, etc.) - Proficiency with AI tools to accelerate model development, analysis, and coding - Experience communicating and collaborating with multidisciplinary stakeholders in a team environment Preferred qualifications - Experience writing and debugging data pipelines - Demonstrated ability to evaluate and receive feedback from mentors, peers, and stakeholders via experience from previous internships or other multi-person projects - Ability to learn new systems and form an understanding of those systems, through independent research and working with a mentor and subject matter experts
Director, Consumer Growth - AI-Native (ATX Preffer...
Director, Consumer Growth - AI-Native (ATX Preffered) at Everly Health. Femtech Insider network role. Industry tags: Consumer Healthcare, Diagnostic Testing, Digital Health, Health, Health Care, Health Diagnostics, Healthcare, Healthcare and Hospitals, Home Care, Lab Testing, Laboratory Services (Healthcare), Medical. Skills: Content Marketing, Media Buying, Data Analysis, Budget Process, Team Leadership. Locations: Multiple locations. Work mode: on site.
Senior Program Officer, AI & Digital Health Delive...
Senior Program Officer, AI & Digital Health Delivery (3-year LTE) at Bill & Melinda Gates Foundation. Femtech Insider network role. Industry tags: Business And Industrial, Charity, Education, Financial Services, Food & Beverage, Gender Equality, Global Development, Global Health, Health Care, Human Resources Hr, Impact Investing, Lending and Investments. Skills: Digital Health Technologies, Primary Health Care, Health Systems, Data Science, Public Health, Investment Management, Problem Solving, Strategic Thinking, Collaboration, Innovation. Locations: Ethiopia, Addis Ababa, Ethiopia. Work mode: on site.
Senior Data Scientist
At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs. If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value. At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs. If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value. The work: We're looking for a Senior Data Scientist to drive analytics across Ripple's product and business portfolio. You'll build the scientific frameworks teams use to evaluate product and business performance, and use AI tooling to accelerate the speed and reach of your analysis. In this role, you'll partner with product and business leads to frame the right questions, bring rigor to how they're answered, and make sure decisions rest on a consistent, thorough foundation. You'll take on ambiguous, high-impact problems, build analyses and tooling others can reuse, and raise the analytical bar of the teams you work with. What you’ll do: - Serve as the data science lead for one or more product or business areas — Payments, Stablecoin, or Custody — applying strong methodology and tackling the hardest analytical problems in your domain. - Partner with product and business leads to shape roadmap decisions: which initiatives to prioritize, what success looks like, and how we'll measure it. - Build the scientific frameworks your teams rely on: product and network health metrics, causal inference approaches, and forecasting that holds up across institutional and developer surfaces. - Apply AI to accelerate analytics — using LLMs and agentic workflows to scale insight generation, automate routine analysis, and make data more self-serve for non-DS partners. - Drive evidence-based evaluation of growth across customers, corridors, and on-chain activity, surfacing the causal drivers behind adoption and volume. - Define and communicate the metrics your teams and leadership run on, translating complex results into clear narratives for senior stakeholders. - Raise the bar for the DS function through mentorship and by modeling strong analytical practice for the data scientists around you. What you'll bring: - 7+ years in data science or quantitative analysis, with a track record of measurable impact on product and business decisions. - Experience as the data science partner to cross-functional teams, shaping roadmaps and helping leaders make better-informed calls. - Track record designing analytics and measurement frameworks that other teams adopt and build on. - Hands-on experience applying AI to accelerate analytics workflows — agentic analysis, AI-assisted insight generation, natural-language data interfaces. - Strong expertise in experimentation, causal inference, forecasting, and statistical modeling. - Expertise in Python or R, fluency in SQL, and experience with large-scale data tech (Databricks). - Experience with FinTech, payments, crypto, or blockchain data is a strong plus. - Advanced degree (MS, PhD) in a quantitative field preferred. - Excellent communication skills — able to translate technical depth into clear narratives for senior stakeholders. WHO WE ARE: Do Your Best Work - The opportunity to build in a fast-paced start-up environment with experienced industry leaders - A learning environment where you can dive deep into the latest technologies and make an impact. A professional development budget to support other modes of learning. - Thrive in an environment where no matter what race, ethnicity, gender, origin, or culture they identify with, every employee is a respected, valued, and empowered part of the team. - In-office collaboration for moments that matter is important to our culture, and we give managers and teams the flexibility to decide which 10+ days a month they come in. - Bi-weekly all-company meeting - business updates and ask me anything style discussion with our Leadership Team - We come together for moments that matter which include team offsites, team bonding activities, happy hours and more! Take Control of Your Finances - Competitive salary, bonuses, and equity - Competitive benefits that cover physical and mental healthcare, retirement, family forming, and family support - Employee giving match - Mobile phone stipend Take Care of Yourself - R&R days so you can rest and recharge - Generous wellness reimbursement and weekly onsite & virtual programming - Generous vacation policy - work with your manager to take time off when you need it - Industry-leading parental leave policies. Family planning benefits. - Catered lunches, fully-stocked kitchens with premium snacks/beverages, and plenty of fun events Benefits listed above are for full-time employees. Ripple is an Equal Opportunity Employer. We’re committed to building a diverse and inclusive team. We do not discriminate against qualified employees or applicants because of race, color, religion, gender identity, sex, sexual identity, pregnancy, national origin, ancestry, citizenship, age, marital status, physical disability, mental disability, medical condition, military status, or any other characteristic protected by local law or ordinance. Please find our UK/EU Applicant Privacy Notice and our California Applicant Privacy Notice for reference.
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