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Staff Machine Learning Engineer, Infrastructure
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. Join our ML Infrastructure engineering team advancing state-of-the-art ultra-realistic multi-agent simulations using foundation models. In this role, you will work at the intersection of ML infrastructure, foundation models, and simulation engineering, with a specific focus on writing high-performance business and simulation logic in JAX/TensorFlow running directly on TPUs to power realistic environments for Reinforcement Learning (RL). What You'll Do - Design, build, and optimize realistic simulation environments and business logic running on TPUs using JAX and TensorFlow. Implement and optimize large-scale model and data parallelism strategies for training and running foundation models on TPU hardware. - Collaborate closely with modeling teams to integrate foundation models into simulation pipelines. - Drive technical architectures and system designs from data engineering through simulation execution to meet business and performance objectives. - Profile systems, identify performance bottlenecks across ML accelerators, and optimize end-to-end execution speed. - Translate product and business goals into concrete technical requirements and system deliverables. Minimum Qualifications - 6+ years of professional software engineering experience, with at least 4 years focused on machine learning infrastructure (scaling, training, optimizing, and deploying large-scale ML systems). - Direct ML programming experience on TPU and GPU hardware using frameworks such as JAX, PyTorch, or TensorFlow. - Proven hands-on experience scaling large models using model parallelism, data parallelism, or distributed training techniques. - Strong understanding of state-of-the-art ML models (e.g., autoregressive transformers) and hands-on proficiency with ML accelerator profiling tools to diagnose bottlenecks. - Demonstrated ability to independently lead ambiguous technical initiatives end-to-end and build robust libraries, pipelines, and developer tooling. - Strong verbal and written communication skills to collaborate effectively across distributed, cross-functional teams. Preferred Qualifications - Practical experience in Reinforcement Learning (RL), Sim2Real transfer, or Robotics. - Experience with distributed ML frameworks and accelerators like GPU/TPU. - Domain familiarity with Autonomous Driving systems, multi-agent simulations, or realistic world modeling. 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
Sr. Machine Learning Engineer, Marketplace ML Plat...
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 Marketplace team builds the core decision systems that keep Waymo running smoothly. They design the algorithms that match riders to vehicles, optimize routing, balance supply and demand, forecast rider demand, and power dynamic pricing. Their work ensures the fleet stays efficient, riders get fast pickups, and the AV network operates safely and profitably across cities. The Platform team is part of the Marketplace engineering: we provide specialized business platforms supporting Marketplace ML and Optimization products. We own and host core ML models used throughout the Marketplace; we automate all parts of the model life cycle, such as feature engineering, training workflows and inference services; we are working on next-generation Marketplace Simulation system capable to run high-fidelity virtual experiments; we also streamline diverse Marketplace Experimentation practices with the goal of providing unified user-friendly Experimentation platform. You will: - Implement and scale the core economic engine for Waymo's ride-hailing commercial services. - Develop high-level infrastructure code for automatically training and deploying sophisticated optimization and prediction ML models to make real-time decisions for pricing, matching, and positioning. You have: - BS degree in Computer Science or equivalent practical experience. - 5+ years of experience programming in backend coding languages such as Java or C++. - Experience in building backend platforms supporting multiple product use-cases/services. - Prior Machine Learning Engineering experience in Python using mature ML frameworks such as TensorFlow, PyTorch or Keras. We prefer: - MS in Computer Science, or equivalent practical experience. - Experience building and deploying ML / Optimization models into production environments. - Experience developing ML data pipelines and ML workflow automation code on top of a mature ML infra. - Experience working at another Ride hailing or Marketplace company. - Coursework background in ML and Optimization. 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
Software Quality Operations Specialist, Safety Eva...
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 Software Quality Operations (SWQOps) team is at the heart of ensuring the safety, reliability, and quality of the Waymo Driver. Our mission is to build an adaptable and scalable operation, increasingly powered by AI, to deliver the crucial insights necessary to confidently deploy and grow Waymo's autonomous vehicle service. Why This Team is Essential to Waymo's Success: Waymo is undergoing unprecedented growth, rapidly expanding into new cities (targeting ~20 new cities by EOY 2026) and launching new vehicle platforms. SWQOps, especially our Technical Specialists, plays a critical role in this expansion, making it possible to scale safely and efficiently. We are on the front lines of: Why This Team is Essential to Waymo's Success: Waymo is undergoing unprecedented growth, rapidly expanding into new cities (targeting ~20 new cities by EOY 2026) and launching new vehicle platforms. SWQOps, especially our Technical Specialists, plays a critical role in this expansion, making it possible to scale safely and efficiently. We are on the front lines of: - Supporting the development of a single, automated, end-to-end machine learning flywheel for the entire Waymo Driver. A successful flywheel will be the core engine for scaling our technology, enabling faster ODD expansion, quicker remediation of driving issues, and a significant reduction in the engineering effort required to maintain and improve the driver. - De-risking New Deployments: Through meticulous triage of driving events, issue discovery, and continuous field monitoring, SWQOps provides early warnings and critical insights. This "early intervention in RO issue detection" ensures operational resilience and safety, particularly in new and complex environments, which is critical as Waymo enters multiple new cities and ramps up platforms like W12. - Enabling Market Expansion: Our team is deeply integrated into every stage of Waymo's market entry framework, from initial city evaluation (OK2Plan) to scaling operations (OK2Scale). We provide the necessary data analysis, policy development, and quality assurance to unblock critical milestones, preventing slowdowns in market expansion velocity. - Driving Engineering Velocity: By handling the vital work of performance evaluation, issue deep-dives, and data set curation, SWQOps collaborates heavily and allows Waymo's Engineering, SysEng, Simulation, and Data Science teams to focus on their core tasks of developing and improving the Waymo Driver. You will: - Provide technical leadership and consultation to stakeholders to enhance our workflows and quality. You'll be at the forefront of identifying and escalating issues with our tools, providing technical requirements to engineering, and driving user testing to support the development and deployment of new tooling features. - Act as the subject matter expert for our Software Quality Operations, working closely with stakeholders, program leads, and vendor teams to ensure seamless adoption and maximum impact of AI/ML advancements in our quality processes. Be the trusted source for creating and updating technical policies, guidelines, and standard operating procedures for new scopes, platforms, and driving signals - Design and implement robust quality control processes for both human and AI-generated outputs. Perform meta-quality checks, validate the integrity of vendor work, and provide feedback to improve both human and model performance. - Partner with Engineering to design, test, and deploy cutting-edge Machine Learning (ML) and Generative AI (Gen-AI) models and tools to drive improvements in issue discovery & detection, triage efficiency, and quality assurance. - Leverage AI-powered insights and traditional triage signals to proactively identify emerging on-road issue trends, new risk scenarios, and edge cases. Develop and refine data-driven strategies for issue discovery and monitoring, enhanced by ML model outputs - Serve as the key link between AI/ML development and operational execution. Define and document new policies, guidelines, and Standard Operating Procedures (SOPs) that integrate AI tools and insights into daily vendor workflows. - Scale workflow from 16/5 to 24/7 coordinating with external stakeholders, vendor team, and FTE team You have: - BS/BA degree and 4 years of relevant work experience in QC or AV Software Quality Operations - Strong understanding of driving rules and regulations, as well kinematic principles critical to safety risk assessment - Increased competency in supporting all phases of the machine learning development lifecycle: data preparation, training, validation, deployment, and continuous monitoring. - Ability to quickly learn and implement new concepts and utilize proprietary tools. - A proven ability to work in a fast-paced, high-stress environment while maintaining good judgment - Excellent communication and interpersonal skills to effectively collaborate with a wide range of individuals in a diverse and dynamic work environment - Demonstrated strong execution with ability to drive outcomes - Ability to effectively and efficiently communicate with cross-functional stakeholders We prefer: - Experience assessing AV safety performance - Experience working with offshore teams / multiple local operations hubs - Competency in LLM / transformer models, and / or ML for robotics domain experience - Basic SQL querying and plx coding experience - Using subject matter expertise for results analysis and direct customer consultation in the development of new and improved solutions - Self-motivated with basic skills in task planning and time management - Experience with ML testing and validation, including dataset quality assurance, bias detection, edge-case scenario testing, and performance evaluation using statistical metrics. The expected base salary range for this full-time position 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. 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 ₹2,900,000 - ₹3,510,000 INR
Senior Technical Program Manager, Simulation
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. Waymo’s Technical Program Managers and Program Managers are accountable for Waymo’s roadmap execution by providing thoughtful cross-functional planning, clarity, and proactive risk management. In the face of complex technical and operational challenges with no established playbooks to follow, we act with thoughtful urgency, driving conversations, discussions, and outcomes. Our team partners closely with every function of Waymo to structure, own and drive work towards real-world deployments of the Waymo Driver across platforms and geographies. This role follows a hybrid work schedule and reports and you will report to a Director of Technical Program Management. You will: - Program Execution: Own and drive the execution of large, cross functional programs within the Simulation and ML Evaluation domain, translating technical roadmaps into structured project plans - ML Integration : Drive the execution of programs that integrate simulation deeply into the ML development loop, establishing a seamless, reliable path for model training, offline evaluation, and validation - Dependency & Risk Management : Proactively identify, track, and mitigate technical dependencies, risks, and blockers across onboard software and offboard infrastructure teams - Technical Judgment: Exercise strong technical judgment to contribute to tool/platform design discussions, evaluate architectural trade offs, and ensure deliverable quality - Operations & Release Coordination : Coordinate releases, manage tool deployment, and oversee triage workflows for simulation platforms to ensure high reliability and usability for developer teams - Process Scaling: Identify systemic bottlenecks in the offline evaluation loop and implement process or tooling improvements to scale simulation capabilities and efficiency You have: - Education : Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience - TPM Experience : 3+ years of experience as a Technical Program Manager in a software engineering or large scale infrastructure environment - ML Experience: 2+ years of experience managing technical projects involving machine learning infrastructure, model training/evaluation loops, or ML data pipelines - Program Ownership : Proven track record of owning and driving complex technical programs end-to-end, managing timelines, risks, and dependencies across multiple stakeholders - Technical Judgment : Strong technical judgment with the ability to engage in design discussions with engineering leads and analyze technical trade offs - Communication : Excellent communication and interpersonal skills, with the ability to convey complex technical concepts to both research and infrastructure teams We prefer: - MLOps & Tooling: Experience with ML frameworks (JAX, PyTorch) and cloud based ML training/evaluation platforms - ML Based Simulation : Familiarity with advanced simulation technologies, including ML based scene generation (e.g. NeRF, Gaussian Splatting, Diffusion, etc.) and reactive agent modeling - Simulation: Familiarity with autonomous system simulation, log playback infrastructure, or closed-loop model validation strategies - Data Management : Experience with large scale dataset management, data validation frameworks, or unified data solutions - Developer Platforms : Experience building or managing developer tools or platforms that simplify complex developer workflows - AV Domain Knowledge : Prior experience in the autonomous vehicle industry or robotics, understanding onboard/offboard dependencies - Advanced Degree : Master's degree in a technical field, ideally related to AI/ML 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 $196,000 - $242,000 USD
Senior Software Engineer, ML Evaluation Infra and...
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 ML Frameworks & Efficiency team partners closely with Research and Production teams across Waymo to develop and deploy models in Perception and Planning that are core to our autonomous driving software. We help our partners by offering the best frameworks for the entire model development lifecycle, including training and evaluation. They are geared towards both scaling models with efficiency and solving problems unique to ML for autonomous driving. We are looking for engineers with ML system expertise to help us train, evaluate and improve pre-trained models to be deployed into Waymo Driver, and potential future products. You’ll work closely with researchers and modeling engineers across the company, and tackle the challenges of different evaluation scenarios for Waymo drivers, building large-scale evaluation platforms that can scale across compute, data, and environments to improve model intelligence and alignment with human drivers. You Will: - Design and build distributed evaluation platforms for large-scale ML evaluation workloads. - Profile evaluation platforms, identify performance bottlenecks (CPU, memory, I/O, network), and implement optimizations to improve inference speed and resource utilization. - Collaborate with ML engineers to understand evaluation requirements and scenarios, and improve DevX of the evaluation infrastructure. - Improve runtime goodput of ML inference workload and efficiency of metrics computations, ensuring scalability and reliability across distributed environments. - Implement and maintain advanced ML infrastructure tools, including ML Pathways, JAX, Flume and TensorFlow. You Have: - B.S. in Computer Science, Math, or 3+ years equivalent real-world experience. - Proficient in distributed systems design with an understanding of ML efficiency. - Experience with ML frameworks, including TensorFlow, JAX, XLA. - Solid programming skills in Python and C++. - Practical familiarity with profiling tools to uncover performance bottlenecks. We Prefer: - MS in Computer Science, Math - Familiarity with large-scale evaluation platforms for LLMs. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $238,000 - $302,000 USD
Senior Software Engineer, DevAI
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. Our mission is to build the next generation of AI-powered developer tools and agentic systems that accelerate the development of the Waymo Driver. We focus on transforming the Software Development Life Cycle (SDLC) through cutting-edge Generative AI and multi-agent frameworks, enabling our engineering teams to build, test, and deploy autonomous driving technology with unprecedented speed and quality. This role follows a hybrid work schedule and reports to the Director of Engineering, Waymo DevAI. You will: - Revolutionize the Inner Loop: Develop multi-agent frameworks and tools designed to enable engineers to write and test code significantly faster, with a focus on spec-driven development and context-aware code generation - Build the Outer Loop: Design and deploy sophisticated agentic flows to automate complex engineering tasks, including root cause analysis agents, automated triage workflows etc - Enhance Driver Debugging: Create specialized agents for perception debugging to accelerate the troubleshooting of complex driving behaviors observed in the real world and simulation - Productionize AI Models: Develop data pipelines and evaluation strategies to deploy state-of-the-art LLMs and agentic solutions into Waymo’s production environment - Collaborate Across Waymo: Partner with engineering teams to understand their unique workflows and drive the adoption of novel DevAI tools You have: - Multi-Agent Systems: Hands-on experience building and deploying multi-agent frameworks and agentic AI systems - SDLC Expertise: Deep understanding of modern software development lifecycles, with a passion for improving the developers velocity and innovation rate - Spec-Driven Development: Proven track record of utilizing spec-driven development methodologies to build robust software - Advanced AI/ML: Strong expertise in Large Language Models (LLMs), including techniques like prompt engineering and Retrieval-Augmented Generation (RAG) - Coding Excellence: Proficiency in architecting and delivering complex, large-scale software systems - Experience: 5+ years software development experience We prefer: - Master's or PhD in Computer Science or a related technical field, with a focus on AI/ML - Experience developing or applying AI-powered tools for software engineering - Deep understanding of modern AI architectures and techniques, including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) - Experience designing, building, and deploying agentic AI systems and frameworks, including multi-agent systems and their application to complex problem domains - Passion for improving developer experiences, and a vision for the future of AI-assisted software development The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD
Senior Machine Learning Engineer, Runtime and Serv...
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 ML Optimization team at Waymo provides a set of tools to support and automate the lifecycle of the machine learning workflow, including feature and experiment management, model development, optimization and monitoring. These efforts have resulted in making machine learning more accessible to teams at Waymo, including Perception, Planner, Research and Simulation. We are looking for engineers with ML software & systems expertise to help b uild the next generation Waymo onboard ML inference engine for Waymo fundamental model. You'll work across the entire ML stack from the system perspective, from efficient deep learning models, model compression, ML software (e.g. JAX, XLA, Triton, and CUDA), to . You will be pleasantly challenged with deploying Waymo ML models on limited computation resources. In this hybrid role, you will report to the Senior Manager of Runtime and Optimization. You will: - Architect and develop an efficient, high-performance ML runtime and serving system tailored for both onboard autonomous vehicle compute and large-scale, offboard data center environments. - Lead the integration and feature development for ML inference runtimes across both domains, balancing the strict real-time latency and memory constraints of onboard systems with the high-throughput, highly concurrent demands of offboard serving fleets. - Drive the strategic migration of ML workloads toward a JAX-native runtime architecture, which includes extending and modifying underlying ML compilers and runtimes (e.g., OpenXLA/PjRT, TensorRT). - Collaborate with world-class Waymo ML practitioners across perception, planner, and research to analyze system-level ML workloads and apply hardware-aware compute optimizations. - Design and build robust tooling for profiling, benchmarking, and identifying system-level bottlenecks across the end-to-end ML software stack. You Have: - B.S. or M.S. in CS, EE, Deep Learning or a related field - 5+ years of professional software engineering experience focused on building, scaling, or maintaining ML systems and infrastructure. - 5+ years production programming in C++. - 3+ years of production experience in Python and major deep learning frameworks (e.g., PyTorch, JAX). - Experience optimizing ML software for hardware accelerators (e.g., GPUs, TPUs, custom silicon). - Experience building low-latency, highly concurrent distributed backend systems. We Prefer - PhD in CS, EE, Deep Learning or a related field. - Experience modifying ML compilers, runtimes, or inference engines (e.g., TensorRT, ONNX Runtime, OpenXLA/PjRT, TVM). - Experience building or scaling LLM serving systems, including expertise in distributed inference and performance optimization (e.g., KV/prefix caching, continuous batching). - Experience with custom kernel development (e.g., CUDA/CUDA Tile, Triton, JAX/Pallas). - Experience architecting unified serving APIs and optimizing tensor buffer management (e.g., zero-copy data transfer, shared memory) for complex, multi-model inference pipelines. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD
Senior Machine Learning Engineer, Predictive Plann...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently. The system architecture team handles the onboard contract of the model with the system, including kinematics, interfaces and representations. Our team’s mission is to work across the stack, building the best setup for the model to drive. We tackle this through projects in data, modeling, metrics, and the overall planner system. In this role, you will report to a Technical Lead Manager. You will: - Tackle challenging real-world problems with ML and engineering solutions. - Use state of the art techniques to design and build ML models, deploy them in the real world on production vehicles - Design and build the necessary architectures, algorithms, pipelines and evaluation systems on Google's extensive data infrastructure - Collaborate with researchers, product area owners and engineers to develop safe, smooth planning behavior for all road users & deliver product requirements. You have: - Bachelors in Computer Science, ML, Robotics, similar technical field of study, or equivalent practical experience - Hands-on experience with modern deep learning libraries (eg: TensorFlow, JAX, Pytorch) - Proficient programming skills (eg: Python, C/C++) - Strong analytical and problem solving skills - 4+ years of experience in Machine Learning modeling and/or Autonomous Vehicles systems We prefer: - MS or PhD in Computer Science, Machine Learning, Robotics, or a related field - Publications in top-tier conferences such as ICML, NeurIPS, CVPR, ICCV, ECCV, ICLR, IROS, CoRL, ACL, or EMNLP - Prior software development or ML research industry experience (including internships) - General software engineering experience solving motion planning or related robotics problems - Experience applying or evaluating ML-based systems in production environments - Experience with performance optimization of deep models, including with respect to specific hardware architectures The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD
Senior Machine Learning Engineer, Fleet Monitoring...
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. You will: - Design, scale, and optimize Waymo's real-time Fleet Monitoring and event response engine to support expansion to global operating locations. - Develop and deploy spatial-temporal anomaly detection models (e.g., S2-cell statistical regressions) and leverage Multimodal Foundation Models (Gemini/VLMs) to detect, triage, and automatically respond to off-nominal operations. - Build and standardize the ML infrastructure for fleet monitoring models, including automated training/inference pipelines, low-latency spatial data stores (e.g., in-memory S2 grids), and continuous model drift monitoring. - Partner with Product Data Scientists to productionize, evaluate, and scale experimental models, translating notebooks and prototype algorithms into production-grade systems. You have: - BS degree in Computer Science or equivalent practical experience. - 6+ years of experience programming in backend coding languages such as Java or C++. - Experience in building backend platforms supporting multiple product use-cases/services. - Prior Machine Learning Engineering experience in Python using mature ML frameworks such as TensorFlow, PyTorch or Keras. We prefer: - MS in Computer Science, or equivalent practical experience. - Experience building and deploying ML / Optimization models into production environments. - Experience developing ML data pipelines and ML workflow automation code on top of a mature ML infra. - Experience working at another Ride hailing or Marketplace company. - Coursework background in ML and Optimization. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $213,000 - $263,000 USD
2027 Summer Intern, MS/PhD, Machine Learning, Simu...
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! You will: - Contribute to existing projects - Implement research ideas into existing projects - Run training and evaluation experiments You have: - Currently enrolled in a Master's or PhD program in Computer Science, Robotics or a related technical field - Python coding - Deep Learning experience with frameworks like TensorFlow, Jax or PyTorch We prefer: - Attention mechanism and autoregressive architectures - Diffusion applied to multimodal data - Experience with autonomous driving and / or robotics Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected annual pay for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Masters Pay £65,000 - £65,000 GBP The expected annual pay for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. PhD Pay £80,000 - £80,000 GBP
Solutions Architect
About Typeface We he lp the world’s biggest brands move from brief to fully personalized campaigns — in days, not months. Founded by Abhay Parasnis and backed by Microsoft, GV, Salesforce, Lightspeed, Madrona and Menlo, we’re building category-defining technology at the intersection of creativity and AI with real impact. Join us to help shape the future of enterprise marketing. What You'll Do As a Solutions Architect, you will translate complex business needs into scalable technical architectures, rapidly prototype AI-driven applications, and integrate Typeface’s platform across customer ecosystems. You’ll combine full-stack engineering expertise with hands-on GenAI experience—building custom agents, workflows, and reusable components—while driving customer transformations through fast experimentation and iterative delivery. How You’ll Make Impact - Partner with Engagement Managers, and Product teams to define customer requirements, facilitate technical workshops, and co-create GenAI solutions - Serve as a Solution Architect during the sales cycle—proposing the right workflows and architecture upfront, getting customer sign-off, then implementing what you sold - Apply an AI-first experimentation mindset—build proof points in days, iterate on feedback, and demonstrate quick wins that build momentum for transformations - Build custom agents and workflows tailored to specific customer use cases - Develop integrations with customer systems (CMS, DAM, marketing automation, analytics) and platforms like Google Workspace, Salesforce, Adobe Experience Cloud, and Campaign Manager 360 - Capture field-proven use cases and funnel them back to Product and Engineering teams to inform the Typeface platform roadmap - Own delivery sprints from requirements through deployment, tackling challenges on-site or remotely while balancing multiple customer engagements - Document playbooks, prompts, patterns, and best practices in internal knowledge bases and create technical guides for common use cases - Share learnings across the organization, mentor other engineers, and present technical demos and case studies at customer events What You’ll Bring - Proven experience designing Solution Architectures for Enterprises and delivering on them via technical delivery - Outstanding customer-centricity with ability to build trust quickly with enterprise technical teams and stakeholders - Exceptional communication skills to translate complex AI concepts for both engineers and executives, present solutions clearly, and write comprehensive technical documentation - Experience with content management systems, digital asset management platforms, marketing automation tools, and creative workflows - Extensive hands-on experience with LLMs (GPT-4, Claude, Llama, Gemini), diffusion models (Stable Diffusion, DALL·E), and multimodal AI for text, images, video, and audio - Proficiency with prompt engineering, fine-tuning, model optimization, and understanding of LLM capabilities, limitations, and hallucination mitigation strategies - Startup DNA: thrives in fast-paced, ambiguous environments with high ownership mentality, comfortable wearing multiple hats and tackling problems outside core expertise Base Salary The OTE compensation range for this role is $160,000-$210,000. Actual compensation may vary based on level, experience, and skillset as assessed in the interview process. Benefits - Competitive compensation — including salary, equity, and 401(k) - Full medical, dental, and vision insurance for you and your family - HSA and FSA options to support your financial wellness - Flexible time off — including parental leave - Well-being programs — resources to support your mental and physical health - Daily lunch & snacks - Mentorship & impact — work closely with top AI leaders on products that ship Equality Opportunity Statement We welcome and encourage applicants from all backgrounds. Employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, medical condition, veteran status, or any other legally protected status. We comply with applicable laws in every jurisdiction where we operate. Why You’ll Love Working Here - Build something big — Be part of a fast-growing startup defining a new category: marketing orchestration powered by Agentic AI. - Your work will matter — Trusted by Fortune 100 companies, our platform delivers 10x content velocity and 90% faster campaigns. - A+ team — Collaborate with veterans from Meta, Amazon, Adobe, Google, Microsoft, and top AI companies. - Backed by the best — GV, Salesforce Ventures, Microsoft, Lightspeed, Madrona and M enlo ($165M raised). - Recognized for innovation — TIME Best Inventions, Fast Company Next Big Thing in Tech, Gartner Cool Vendor, Adweek AI Company of the Year, LinkedIn Top Startup, Webby Award (AI Work & Productivity).
Senior ML Engineer
About Typeface We he lp the world’s biggest brands move from brief to fully personalized campaigns — in days, not months. Founded by Abhay Parasnis and backed by Microsoft, GV, Salesforce, Lightspeed, Madrona and Menlo, we’re building category-defining technology at the intersection of creativity and AI with real impact. Join us to help shape the future of enterprise marketing. What You’ll Do As a Senior Machine Learning Engineer , you will be responsible for building machine learning models/systems and innovative web applications that deliver the power of Generative AI to our customers. You will work closely with designers, product managers and other engineers driving the technical designs and implementations. How You'll Make an Impact - Define the roadmap for generation of various content types including text, image, audio and video enabling us to generate brand-affinized, hyper-personalized, and safe content - Own the delivery of key business, product and technology initiatives across design, specification, development, testing, and launch. - Establish and implement processes for model training, fine-tuning, testing, and operating the Generative AI models at significant scale. - Investigate, prototype and deliver innovative solutions - Collaborate with other engineers, Product and Business Teams to identify new ways to improve our product and drive growth - Establish a data-driven development culture by driving the definition, tracking, and operationalizing of feature, product, and company-level metrics What You Bring - Master’s or PhD degree in Computer Science or a related field - Experience with building and evolving ML Training and Inferencing systems at significant scale with a purpose of solving real-world problems. - 5+ years of prior experience designing, building and scaling systems for training, testing, and running Machine Learning models at scale - Have a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects - Proficiency in Python, C/C++ (or equivalent) - Ability to create self-contained, reusable, and testable modules and passionate about prototyping - Prior experience with AzureML, GCP Vertex, AWS Sagemaker or similar platforms - Bonus: Hands-on experience in deploying and tuning Generative AI models in cloud or on local system - Bonus: Experience working at a high-growth startup or tech company - Publications and research experiences in ML venues and/or vision/language applications are a plus Location This is a hybrid role based in our Palo Alto HQ. We collaborate in-office 3 days a week. Base Salary The range for this role is $188,000-$200,000. Actual compensation may vary based on level, experience, and skillset as assessed in the interview process. Benefits - Competitive compensation — including salary, equity, and 401(k) - Full medical, dental, and vision insurance for you and your family - HSA and FSA options to support your financial wellness - Flexible time off — including parental leave - Well-being programs — resources to support your mental and physical health - Daily lunch & snacks - Mentorship & impact — work closely with top AI leaders on products that ship Equality Opportunity Statement We welcome and encourage applicants from all backgrounds. Employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, medical condition, veteran status, or any other legally protected status. We comply with applicable laws in every jurisdiction where we operate. Why You’ll Love Working Here - Build something big — Be part of a fast-growing startup defining a new category: marketing orchestration powered by Agentic AI. - Your work will matter — Trusted by Fortune 100 companies, our platform delivers 10x content velocity and 90% faster campaigns. - A+ team — Collaborate with veterans from Meta, Amazon, Adobe, Google, Microsoft, and top AI companies. - Backed by the best — GV, Salesforce Ventures, Microsoft, Lightspeed, Madrona and M enlo ($165M raised). - Recognized for innovation — TIME Best Inventions, Fast Company Next Big Thing in Tech, Gartner Cool Vendor, Adweek AI Company of the Year, LinkedIn Top Startup, Webby Award (AI Work & Productivity).
Staff Research Scientist
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing is seeking exceptional Research Scientists to join our research organization and develop new ways to evaluate, train, and improve frontier AI systems. This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication. Our research is deliberately focused on frontier evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them. What You'll Do Frontier benchmarks and evaluation - Identify high-impact gaps in existing benchmark and evaluation literature. - Design novel benchmarks in and across STEM fields and on general model functionality. - Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks. - Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies. - Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models. Synthetic data and post-training - Develop methods for generating high-quality synthetic STEM training data. - Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance. - Explore methods for generating useful training signal in domains where expert human data is scarce or expensive. - Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume. Hallucination, reliability, and verification - Study hallucination, uncertainty, calibration, and epistemic failure in technical domains. - Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention. - Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know. Agentic science - Research AI systems capable of performing extended scientific and technical work. - Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning. - Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research. - Explore new approaches to human-AI and multi-agent scientific collaboration. New research directions The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities. What We’re Looking For - PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field . - Demonstrated ability to formulate and execute original research. - Strong understanding of modern LLMs and the frontier AI research landscape. - Excellent experimental design, quantitative reasoning, and scientific judgment. - Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas. - Strong Python skills and the ability to independently build research prototypes and evaluation pipelines. - Excellent technical writing and communication. - Comfort working in a fast-moving environment where the research agenda evolves with the frontier. A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter . A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $400,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Staff Research Engineer, Frontier Data
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: - Environments for Software Engineering / coding agents - UI-Environments for Computer-Use/Browser-Use agents - MCP-based Environments for general function-calling agents across various enterprise and consumer applications We are seeking Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training. You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications. This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. What You’ll Do 1. Conduct Research on Frontier AI Systems - Investigate the capabilities, limitations, and training methods of frontier AI systems. - Formulate research questions that can inform Turing’s products, platforms, and technical strategy. - Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. - Stay current with advances in machine learning and identify opportunities for meaningful technical contribution. 2. Build and Evaluate Research Systems - Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks. - Train, test, and evaluate models using modern AI and machine learning tools. - Analyze results carefully and draw clear, evidence-based conclusions. - Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation. - Iterate quickly from early hypothesis through validated technical insight. 3. Translate Research into Practical Impact - Collaborate closely with Research, Engineering, Product, and Operations teams. - Translate research findings into improvements for Turing’s products, platforms, and AI capabilities. - Help identify which ideas are ready to move from exploration into scalable, real-world applications. - Communicate technical findings clearly to both specialized and cross-functional audiences. 4. Contribute to the Research Community - Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate. - Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration. - Represent Turing thoughtfully within the broader AI research community. What We’re Looking For - Research background: PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered. - Technical depth: Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling. - Relevant expertise: Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks. - Research engineering ability: Strong programming skills and the ability to implement, test, and iterate quickly in a research environment. - Scientific judgment: Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making. - Communication and collaboration: Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams. A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $400,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Staff Research Engineer, Enterprise Knowledge
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: - Environments for Software Engineering / coding agents - UI-Environments for Computer-Use/Browser-Use agents - MCP-based Environments for general function-calling agents across various enterprise and consumer applications We are seeking Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training. You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications. This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. What You’ll Do 1. Conduct Research on Frontier AI Systems - Investigate the capabilities, limitations, and training methods of frontier AI systems. - Formulate research questions that can inform Turing’s products, platforms, and technical strategy. - Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. - Stay current with advances in machine learning and identify opportunities for meaningful technical contribution. 2. Build and Evaluate Research Systems - Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks. - Train, test, and evaluate models using modern AI and machine learning tools. - Analyze results carefully and draw clear, evidence-based conclusions. - Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation. - Iterate quickly from early hypothesis through validated technical insight. 3. Translate Research into Practical Impact - Collaborate closely with Research, Engineering, Product, and Operations teams. - Translate research findings into improvements for Turing’s products, platforms, and AI capabilities. - Help identify which ideas are ready to move from exploration into scalable, real-world applications. - Communicate technical findings clearly to both specialized and cross-functional audiences. 4. Contribute to the Research Community - Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate. - Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration. - Represent Turing thoughtfully within the broader AI research community. What We’re Looking For - Research background: PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered. - Technical depth: Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling. - Relevant expertise: Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks. - Research engineering ability: Strong programming skills and the ability to implement, test, and iterate quickly in a research environment. - Scientific judgment: Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making. - Communication and collaboration: Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams. A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $400,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Staff Research Engineer
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: - Environments for Software Engineering / coding agents - UI-Environments for Computer-Use/Browser-Use agents - MCP-based Environments for general function-calling agents across various enterprise and consumer applications We are seeking Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training. You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications. This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. What You’ll Do 1. Conduct Research on Frontier AI Systems - Investigate the capabilities, limitations, and training methods of frontier AI systems. - Formulate research questions that can inform Turing’s products, platforms, and technical strategy. - Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. - Stay current with advances in machine learning and identify opportunities for meaningful technical contribution. 2. Build and Evaluate Research Systems - Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks. - Train, test, and evaluate models using modern AI and machine learning tools. - Analyze results carefully and draw clear, evidence-based conclusions. - Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation. - Iterate quickly from early hypothesis through validated technical insight. 3. Translate Research into Practical Impact - Collaborate closely with Research, Engineering, Product, and Operations teams. - Translate research findings into improvements for Turing’s products, platforms, and AI capabilities. - Help identify which ideas are ready to move from exploration into scalable, real-world applications. - Communicate technical findings clearly to both specialized and cross-functional audiences. 4. Contribute to the Research Community - Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate. - Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration. - Represent Turing thoughtfully within the broader AI research community. What We’re Looking For - Research background: PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered. - Technical depth: Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling. - Relevant expertise: Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks. - Research engineering ability: Strong programming skills and the ability to implement, test, and iterate quickly in a research environment. - Scientific judgment: Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making. - Communication and collaboration: Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams. A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $400,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Senior Software Engineer
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . ABOUT THE ROLE Turing is the world's leading research accelerator for frontier AI labs. The STEM Horizontal team produces the high-signal STEM data those labs train on, and this role builds the machinery behind it: the task-authoring tools, data pipelines, evaluation harnesses, sandboxed execution environments, and agentic workflows our researchers and domain experts work inside every day. This sits at the seam between engineering and research. You'll own production systems end-to-end, and you'll be the person a researcher pulls in to ask why an eval is producing misleading numbers. Correctness and reproducibility are the product: a subtle bug here doesn't crash anything, it quietly poisons a training run and costs weeks. If you want to build systems that visibly shape how frontier models learn to reason about code and science, this is the seat. WHAT YOU'LL DO - Own services end-to-end: design, implementation, rollout, and production operation. - Build backend services and the data pipelines that generate, validate, score, and version datasets, reproducible from a commit and a config. - Ship data-dense internal interfaces: virtualised tables, server-side filtering, review and annotation UIs that stay fast at tens of thousands of rows. - Build and operate agent harnesses , multi-step loops with tool use, retries, and structured output, running against real repos and test suites. - Build sandboxed execution environments where model-generated code runs safely and deterministically at volume, and stand up the RL environments and eval harnesses on top. - Trace and debug agent runs end-to-end: where the loop stalled, which tool call failed, why the grader disagreed with the human. - Own infrastructure as code and CI/CD; debug production issues across the stack and drive the reliability work that follows. - Raise the bar through code review, design docs, and innovation, partnering with researchers and quality owners on what “good” means. TECH STACK Backend Python · FastAPI · Django / Django Ninja · PostgreSQL · MongoDB · REST + OpenAPI Frontend TypeScript · React · Next.js · virtualised tables, server-side filtering Platform Linux · Docker · Terraform · CI/CD · AWS or GCP WHAT YOU NEED - 6+ years building and operating production software, ideally at product companies with real scale. - Deep Python with production FastAPI and/or Django , ORM performance, migrations, async patterns. - Strong PostgreSQL (schema design, query optimisation, indexing) and NoSQL. - Frontend production with TypeScript / React / Next.js . - Fluent on Linux and Docker ; Terraform or comparable IaC on AWS or GCP , and ownership of the CI/CD that ships it. - Daily use of LLM coding tools (Claude Code, Cursor, Copilot) as part of how you actually ship, with the judgement to review their output critically and know where they break down. We are an AI-forward team; this is a must-have. - You've built something on top of LLM APIs that other people depended on, an agent, a pipeline, an eval, and you know where it was fragile. - Comfort with non-deterministic systems : you can tell a real regression from noise, and you reach for a controlled experiment over a hunch. - Advanced Git and real testing discipline, unit, integration, and the negative and edge cases people skip. - Clear written communication; comfortable working without hand-holding, and comfortable saying when a spec is wrong. NICE TO HAVE - Internal tools or developer platforms used daily by a technical team. - Agent frameworks and protocols, LangGraph, MCP, OpenAI/Anthropic tool use. - Sandboxing and untrusted code execution: gVisor, Firecracker, seccomp. - Workflow orchestration (Temporal, Airflow, Prefect, Dagster) and Kubernetes beyond managed defaults. - LLM coding benchmarks (SWE-bench, Terminal-Bench), eval frameworks, or exposure to RLHF / RLVR. PREFERRED BACKGROUND Bachelor's or Master's degree with 6+ years experience in Computer Science, Software Engineering, Data Science, Machine Learning, AI, or a programming-heavy IT field. We weight demonstrated work outcomes most heavily, a strong track record without a matching degree is fine. Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Senior Research Scientist, STEM
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing is seeking exceptional Research Scientists to join our STEM research organization and develop new ways to evaluate, train, and improve frontier AI systems. This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication. Our research is deliberately focused on frontier STEM evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them. What You'll Do Frontier benchmarks and evaluation - Identify high-impact gaps in existing benchmark and evaluation literature. - Design novel benchmarks in and across STEM fields and on general model functionality. - Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks. - Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies. - Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models. Synthetic data and post-training - Develop methods for generating high-quality synthetic STEM training data. - Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance. - Explore methods for generating useful training signal in domains where expert human data is scarce or expensive. - Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume. Hallucination, reliability, and verification - Study hallucination, uncertainty, calibration, and epistemic failure in technical domains. - Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention. - Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know. Agentic science - Research AI systems capable of performing extended scientific and technical work. - Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning. - Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research. - Explore new approaches to human-AI and multi-agent scientific collaboration. New research directions The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities. What We’re Looking For - PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field . - Demonstrated ability to formulate and execute original research. - Strong understanding of modern LLMs and the frontier AI research landscape. - Excellent experimental design, quantitative reasoning, and scientific judgment. - Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas. - Strong Python skills and the ability to independently build research prototypes and evaluation pipelines. - Excellent technical writing and communication. - Comfort working in a fast-moving environment where the research agenda evolves with the frontier. A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter . A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $350,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Senior Research Engineer, SWE
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: - Environments for Software Engineering / coding agents - UI-Environments for Computer-Use/Browser-Use agents - MCP-based Environments for general function-calling agents across various enterprise and consumer applications We are seeking Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training. You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications. This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. What You’ll Do 1. Conduct Research on Frontier AI Systems - Investigate the capabilities, limitations, and training methods of frontier AI systems. - Formulate research questions that can inform Turing’s products, platforms, and technical strategy. - Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. - Stay current with advances in machine learning and identify opportunities for meaningful technical contribution. 2. Build and Evaluate Research Systems - Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks. - Train, test, and evaluate models using modern AI and machine learning tools. - Analyze results carefully and draw clear, evidence-based conclusions. - Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation. - Iterate quickly from early hypothesis through validated technical insight. 3. Translate Research into Practical Impact - Collaborate closely with Research, Engineering, Product, and Operations teams. - Translate research findings into improvements for Turing’s products, platforms, and AI capabilities. - Help identify which ideas are ready to move from exploration into scalable, real-world applications. - Communicate technical findings clearly to both specialized and cross-functional audiences. 4. Contribute to the Research Community - Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate. - Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration. - Represent Turing thoughtfully within the broader AI research community. What We’re Looking For - Research background: PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered. - Technical depth: Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling. - Relevant expertise: Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks. - Research engineering ability: Strong programming skills and the ability to implement, test, and iterate quickly in a research environment. - Scientific judgment: Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making. - Communication and collaboration: Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams. A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $350,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Senior Research Engineer, Frontier Data
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: - Environments for Software Engineering / coding agents - UI-Environments for Computer-Use/Browser-Use agents - MCP-based Environments for general function-calling agents across various enterprise and consumer applications We are seeking Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training. You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications. This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. What You’ll Do 1. Conduct Research on Frontier AI Systems - Investigate the capabilities, limitations, and training methods of frontier AI systems. - Formulate research questions that can inform Turing’s products, platforms, and technical strategy. - Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. - Stay current with advances in machine learning and identify opportunities for meaningful technical contribution. 2. Build and Evaluate Research Systems - Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks. - Train, test, and evaluate models using modern AI and machine learning tools. - Analyze results carefully and draw clear, evidence-based conclusions. - Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation. - Iterate quickly from early hypothesis through validated technical insight. 3. Translate Research into Practical Impact - Collaborate closely with Research, Engineering, Product, and Operations teams. - Translate research findings into improvements for Turing’s products, platforms, and AI capabilities. - Help identify which ideas are ready to move from exploration into scalable, real-world applications. - Communicate technical findings clearly to both specialized and cross-functional audiences. 4. Contribute to the Research Community - Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate. - Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration. - Represent Turing thoughtfully within the broader AI research community. What We’re Looking For - Research background: PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered. - Technical depth: Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling. - Relevant expertise: Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks. - Research engineering ability: Strong programming skills and the ability to implement, test, and iterate quickly in a research environment. - Scientific judgment: Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making. - Communication and collaboration: Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams. A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $350,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Senior Research Engineer
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: - Environments for Software Engineering / coding agents - UI-Environments for Computer-Use/Browser-Use agents - MCP-based Environments for general function-calling agents across various enterprise and consumer applications We are seeking Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training. You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications. This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. What You’ll Do 1. Conduct Research on Frontier AI Systems - Investigate the capabilities, limitations, and training methods of frontier AI systems. - Formulate research questions that can inform Turing’s products, platforms, and technical strategy. - Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. - Stay current with advances in machine learning and identify opportunities for meaningful technical contribution. 2. Build and Evaluate Research Systems - Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks. - Train, test, and evaluate models using modern AI and machine learning tools. - Analyze results carefully and draw clear, evidence-based conclusions. - Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation. - Iterate quickly from early hypothesis through validated technical insight. 3. Translate Research into Practical Impact - Collaborate closely with Research, Engineering, Product, and Operations teams. - Translate research findings into improvements for Turing’s products, platforms, and AI capabilities. - Help identify which ideas are ready to move from exploration into scalable, real-world applications. - Communicate technical findings clearly to both specialized and cross-functional audiences. 4. Contribute to the Research Community - Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate. - Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration. - Represent Turing thoughtfully within the broader AI research community. What We’re Looking For - Research background: PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered. - Technical depth: Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling. - Relevant expertise: Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks. - Research engineering ability: Strong programming skills and the ability to implement, test, and iterate quickly in a research environment. - Scientific judgment: Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making. - Communication and collaboration: Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams. A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $350,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Research Scientist, STEM
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing is seeking exceptional AI Research Scientists to join our STEM research organization and develop new ways to evaluate, train, and improve frontier AI systems. This is a research-first role focused on problems where the right benchmark, dataset, or methodology often does not yet exist. You will identify important gaps in the literature, propose ambitious new research directions, and take projects from initial hypothesis through experimentation, benchmark construction, and publication. Our research is deliberately focused on frontier STEM evaluation, synthetic data, hallucination and reliability, and agentic science . We are looking for scientists who can recognize important problems early, formulate them precisely, and design rigorous research programs to answer them. What You'll Do Frontier benchmarks and evaluation - Identify high-impact gaps in existing benchmark and evaluation literature. - Design novel benchmarks in and across STEM fields and on general model functionality. - Develop evaluations for emerging model capabilities that are poorly captured by traditional static benchmarks. - Design rigorous task-generation, grading, contamination-control, difficulty-calibration, and validation methodologies. - Build benchmarks that can become both valuable research contributions and meaningful standards for evaluating frontier models. Synthetic data and post-training - Develop methods for generating high-quality synthetic STEM training data. - Study how task selection, difficulty, diversity, verification, filtering, and data quality affect downstream performance. - Explore methods for generating useful training signal in domains where expert human data is scarce or expensive. - Design experiments that determine when synthetic data genuinely improves capabilities rather than simply increasing training volume. Hallucination, reliability, and verification - Study hallucination, uncertainty, calibration, and epistemic failure in technical domains. - Develop evaluations and methods for improving factual reliability, self-correction, verification, citation, and appropriate abstention. - Investigate when models should reason internally, invoke tools, seek external evidence, or recognize that they do not know. Agentic science - Research AI systems capable of performing extended scientific and technical work. - Develop workflows involving literature search, coding, simulation, tool use, experimentation, verification, and iterative reasoning. - Evaluate long-horizon scientific agents and identify the bottlenecks preventing them from reliably performing real research. - Explore new approaches to human-AI and multi-agent scientific collaboration. New research directions The areas above are our core focus, not an exhaustive list. Researchers will also have significant latitude to propose new programs in areas such as reasoning, model evaluation, AI-for-science, data generation, and emerging capabilities. What We’re Looking For - PhD or equivalent research experience in machine learning, computer science, mathematics, physics, chemistry, biology, engineering, statistics, or another highly technical field . - Demonstrated ability to formulate and execute original research. - Strong understanding of modern LLMs and the frontier AI research landscape. - Excellent experimental design, quantitative reasoning, and scientific judgment. - Ability to rapidly understand unfamiliar technical literature and develop expertise in new areas. - Strong Python skills and the ability to independently build research prototypes and evaluation pipelines. - Excellent technical writing and communication. - Comfort working in a fast-moving environment where the research agenda evolves with the frontier. A strong publication record is valuable, but we care most about whether you can identify important questions, design rigorous ways to answer them, and execute quickly enough for the results to matter . A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing - Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. - Build datasets and environments that directly improve the capabilities of advanced AI systems. - Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. - Apply frontier AI innovations to high-value enterprise workflows. - Operate with high autonomy, rapid iteration, and meaningful commercial impact. - Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. - Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $150,000 to $300,000 OTE + Equity Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
AI Engagement Lead
About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . About the role: Turing is looking for people with GenAI experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization leading a GenAI project end-to-end from POC to full-scale implementation and you will be leading a team of other Turing engineers across different skill sets. In the past, the Turing GenAI delivery organization has implemented industry-leading multi-agent LLM systems, RAG systems, and Open Source LLM deployments for major enterprises. Role responsibilities - Client Relationship and Communication: - Lead discussions with clients and prospects to build AI product pipeline and expand to more processes. - Prioritize client engagement, strengthen relationships, and manage expectations proactively. - Act as a point of contact for client communication, feedback and escalations. - Evaluate and select use-cases for new product development and unlock new revenue opportunities for the client - Participate in client’s internal stakeholder meetings (when applicable) to capture, clarify, and consolidate requirements across functions, ensuring all inputs are properly documented and mapped into actionable product needs. - Tech Leadership: - Provide technical direction to the engineering team on appropriate solutioning & system design for a given problem statement - Align the engineering team toward a technical roadmap and ensure timely execution of the roadmap to achieve customer satisfaction. - Study new LLM research topics and engage in conversation on same with client stakeholders to showcase technical prowess and build value for Turing. - Team Leadership and Coordination: - Drive cross-functional alignment across engineering, product, and client success teams. - Remove blockers for both the client & internal teams by ensuring smooth communication and effective prioritization flows. - Conduct regular 1:1 meetings focused on support, expectations, delivery alignment, and general well-being. - Project Planning and Oversight: - Manage the big-picture program timeline (releases, phases, go-live plans) using engineering velocity and capacity inputs provided by the Engagement Manager. - Co-own delivery planning by managing the program-level timeline and facilitating Sprint Planning sessions ensuring release milestones and sprint goals are aligned with business priorities, clearly defined requirements, and the engineering capacity and feasibility inputs provided by the Engagement Manager. - Own overall delivery to ensure quality, scope, and timelines are consistently met. - Ensure robust business-facing documentation , including requirements documents, BRDs/PRDs, implementation plans, product roadmaps, and client-facing marketing or enablement materials. - Ensure delivery decisions reflect an understanding of operational costs, ROI, and long-term business impact . - Identify delivery risks , create proactive mitigation plans, and track program health across all milestones with keen insight. - Acknowledge new client requests promptly and partner with the Engagement Manager to assess feasibility, capacity, and timeline impact before providing any commitment to the client. Required skills - Excellent communication skills & stakeholder management to effectively collaborate with client SMEs - Entrepreneurial & founders mindset to own end-to-end GenAI project deliveries and guide customers through various phases - 10+ years of professional experience with at least 4+ years in driving Machine Learning & AI projects - Good understanding of the latest GenAI agentic designs and frameworks like Langchain, Langgraph, etc. - Understanding of Cloud services including Azure, GCP, or AWS - Understanding of Agile methodologies and project management tools like JIRA, Confluence. Compensation Range : $240k - $260K Values - We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. - We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection - We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing - Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. - Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. - Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. - Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. - Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so 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 anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .
Research, General Agents
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 This role is responsible for advancing the agentic capabilities of our models, with ownership spanning the full development cycle. Our research team is small, and the role carries a corresponding degree of autonomy and responsibility. Note: This is an "evergreen role" that we keep open on an on-going basis to express interest in this research area. 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 - Build a synthetic data framework used across the team, and create new task environments to scale training across agentic capabilities such as tool use, long-horizon tasks, and complex workflows. - Address identified gaps through data and recipe work, validating proposed changes through controlled ablations. - Enhance usability for agent capabilities end to end, translating internal and external feedback into model improvements. SKILLS AND QUALIFICATIONS Minimum qualifications: - Strong engineering skills, ability to contribute code and debug in complex codebases. - Ability to design, run, and interpret experiments with scientific rigor and clarity. - 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. - Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding. - Clarity in communication, an ability to explain complex technical concepts in writing. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but at least some: - Experience building synthetic data pipelines and systems that were adopted by others on your team and remain in use today. - Experience owning the end-to-end cycle of identifying gaps in model usability and closing them through custom evaluations and training data. - Experience making large-scale agentic RL infrastructure reliable given the long tail of failures that surface at scale. - Experience improving the agentic capabilities of a frontier model. - 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 Software Engineer, Post Training
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 This role is responsible for building and strengthening the engineering foundations that our post-training research teams depend on. You'll embed within a research team, and build or improve the systems required for the team to succeed. Our research teams are small, and the role carries a corresponding degree of autonomy and responsibility. WHAT YOU’LL DO - Embed within a research team to build, harden, and improve the systems and infrastructure required for the team to succeed. - Design, build, and operate infrastructure research teams depend on, including RL training systems, sandboxing, data pipelines, and agent scaffolding. 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 leading projects end to end, working in large fast-moving codebases, and writing code others have depended and built on. - Strong proficiency in Python and strong engineering fundamentals, with experience debugging systems that fail intermittently and at scale. - Clarity in communication, an ability to explain complex technical concepts in writing. - Strong autonomous drive to progress towards the team’s goals with an ownership mindset. Preferred qualifications — we encourage you to apply even if you don’t meet all preferred qualifications, but preferably some: - Experience building or iterating with sandboxed or containerized execution environments at a large scale. - Experience in a role where the team's priorities set yours, and a track record of success in such a role. - Familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). 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. As set forth in Thinking Machines' Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.
Research Engineer, Infrastructure, Numerics
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 and build the core systems that enable efficient large-scale model training with a focus on numerics. You will focus on improving the numerical foundations of our distributed training stack, from precision formats and kernel optimizations to communication frameworks that make training trillion-parameter models stable, scalable, and fast. This role is ideal for someone who thrives at the intersection of research and systems engineering: a builder who understands both the math of optimization and the realities of distributed compute. 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 - Design and optimize distributed training infrastructure for large-scale LLMs, focusing on performance, stability, and reproducibility across multi-GPU and multi-node setups. - Implement and evaluate low-precision numerics (for example, BF16, MXFP8, NVFP4) to improve efficiency without sacrificing model quality. - Develop kernels and communication primitives that use hardware-level support for mixed and low-precision arithmetic. - Collaborate with research teams to co-design model architectures and training recipes that align with emerging numeric formats and stability constraints. - Prototype and benchmark scaling strategies such as data, tensor, and pipeline parallelism that integrate precision-adaptive computation and quantized communication. - Contribute to the design of our internal orchestration and monitoring systems to ensure that thousands of distributed experiments can run efficiently and reproducibly. - 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, electrical engineering, statistics, machine learning, physics, robotics, or similar. - Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures. - 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 in areas such as floating-point numerics, low-precision arithmetic, and distributed systems. Preferred qualifications — we encourage you to apply if you meet some but not all of these: - Familiarity with distributed frameworks such as PyTorch/XLA, DeepSpeed, Megatron-LM. - Experience implementing FP8, INT8, or block-floating point (MX) formats and understanding their numerical trade-offs. - Prior contributions to open-source deep learning infrastructure such as PyTorch, DeepSpeed, or XLA. - Publications, patents, or projects related to numerical optimization, communication-efficient training, or systems for large models. - Experience training and supporting large-scale AI models. - 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.
Product Manager - Post Training
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 This is a product role for someone who can operate inside frontier research without trying to turn research into a conventional software roadmap. The work requires judgment, technical fluency, user empathy, discretion, and the ability to create clarity without creating bureaucracy. The Post-Training Product Manager will work as a high-trust partner to our post-training researchers. Your role is to understand the work deeply enough to ask the right questions, identify missing connections, surface implications, and help the team decide what matters next. The role sits at the seam between research, model behavior, data and environments, evaluations, training and inference infrastructure, safety, product, and the people using our models. Inkling was post-trained across math, agentic code and tool use, audio, image, chat, and safety, with a large-scale asynchronous RL program that exceeded 30M rollouts. Inkling and Inkling-Small share a scalable post-training stack, and Inkling is available for customization on Tinker. The next phase requires tight loops among research priorities, model behavior, evaluations, infrastructure constraints, user and customer learning, and product direction. The person in this role will help those loops compound instead of fragment — maintaining the bird's-eye view while researchers go deep: where work is converging, where teams are solving adjacent problems without enough shared context, what evidence is missing, what decisions are blocked, and how research becomes a stronger model and a useful product. What You'll Do - Partner with post-training research leaders on priorities, sequencing, decision points, and the connection between research work and the broader model and product agenda - Maintain a clear view across SFT, RL, data and environments, evaluations, safety, model behavior, inference, training infrastructure, and product dependencies; identify gaps before they become blockers - Translate ambiguous research and product questions into concrete learning plans: what must be true, what evidence would change the decision, which experiments or user signals matter, and when the team should revisit the direction - Build lightweight operating mechanisms for critical work — owners, state, dependencies, decisions, risks, release criteria, and follow-through — without imposing a software-development process on research - Bring qualitative and quantitative evidence about model behavior and real workflows into research prioritization, ensuring user knowledge is represented accurately rather than flattened into feature requests - Connect research, product, infrastructure, safety, and leadership when a decision spans teams or when local optimization creates a broader product or model tradeoff - Support the path from research result to usable capability: internal adoption, evaluation, documentation, release readiness, product integration, and feedback after launch - Write clear narratives that explain what the team has learned, what remains uncertain, what decisions are needed, and why the work matters - Take on the unowned work that is necessary to move a critical research-product outcome forward Skills and Qualifications Minimum qualifications: - Experience as an early or first product leader in a technical startup, AI lab, research organization, or new product area where the role and operating model were not defined for you - Experience working closely with model training, post-training, RL, evaluations, data, safety, inference, developer platforms, or another technically demanding research-product area - Ability to understand research deeply enough to earn trust, ask sharp questions, and connect technical choices to model behavior and users without overstating your expertise - Strong track record identifying the missing connection, unresolved assumption, or cross-team decision that specialists may not see while deep in the work Preferred qualifications: - Ability to represent user and product truth in a research environment without reducing research to a list of customer requests - Comfortable making progress when the goal, metric, or path is still evolving, and the correct next step may be a learning loop rather than a launch - Clear communicator who handles disagreement without ego and is willing to change a recommendation when the evidence changes - Motivated by senior IC ownership and proximity to the work more than a large PM team or a conventional product ladder - Background as a research product leader or early AI product leader at a frontier lab, model company, or technically ambitious startup; a technical founder, former engineer, or applied scientist who moved into product; a PM for model training, post-training, evaluation platforms, data systems, ML infrastructure, or developer platforms; or the first PM at a company that turned a novel technical capability into a product, category, or developer ecosystem Logistics - Location: This role is based in San Francisco, CA. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $400,000 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.
Developer Relations Engineer
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 TINKER & INKLING Tinker https://thinkingmachines.ai/tinker/ is our fine-tuning API that empowers researchers and developers to customize frontier AI to their needs — opening access to capabilities that have previously been concentrated in a handful of labs. We manage the infrastructure while allowing Tinkerers full flexibility in training open weights models with their own data, algorithms, and for their own needs. Inkling https://thinkingmachines.ai/news/introducing-inkling/ is our open-weights model. It is a Mixture-of-Experts transformer with 975B total parameters, 41B active. It supports a context window of up to 1M tokens. It was pretrained on 45 trillion tokens of text, images, audio and video. It is the first in a family of models of different sizes. ABOUT THE ROLE We're hiring a Developer Relations Engineer to help the open source community build with Thinking Machines' products — Inkling https://thinkingmachines.ai/news/introducing-inkling/ and the models that follow it, and Tinker http://tinker, our fine-tuning platform. You'll work hands-on with real users, turn their feedback into product improvements, and help developers get the most out of our models and tools. This role sits at the intersection of engineering, research and community: you'll write code, ship industry leading ML recipes and integrations, and also represent Thinking Machines externally through talks, demos, and technical writing. It's a great fit for someone who's happiest building in a code editor, but who also enjoys teaching and engaging with a fast-growing developer community. WHAT YOU'LL DO - Write, test, and maintain high-quality agentic training, code examples and documentation for Inkling and Tinker - Work directly with developers and researchers in the open source community to debug issues, gather feedback, and improve their experience fine-tuning and deploying models - Partner with the research and infrastructure teams to turn community patterns and feedback into concrete improvements to Inkling, future models, Tinker, and develop new post-training recipes. - Build demos, integrations, and tools that make it easier for developers to adopt and get value from Thinking Machines' models - Represent Thinking Machines in the open source community through blog posts, talks and tutorials, livestreams, and conference and meetup appearances - Help shape the developer experience roadmap as new models and platform features ship SKILLS & QUALIFICATIONS MINIMUM QUALIFICATIONS - 5+ years of experience in developer relations, developer experience, or a software engineering role with significant open source or community-facing work - Strong software engineering skills, with experience writing and shipping production-quality Python code - Hands-on experience fine-tuning, evaluating, or deploying large language models PREFERRED QUALIFICATIONS - Experience with supervised and reinforcement learning fine-tuning of LLMs, post-training on new domains, and improving model capability - Track record contributing to, maintaining, or growing open source software libraries - Experience writing public or internal tutorials, cookbooks, or technical content that helped others understand and apply technical concepts - Comfortable engaging publicly through talks, livestreams, or conference and meetup appearances - Familiarity with multimodal models (text, image, audio) and the tradeoffs involved in training, fine-tuning, and inference 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 $300,000 - $350,000 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.
Member of Technical Staff- Research Engineer (Harn...
Tessl is a fast-growing Series A startup based in London, founded by Guy Podjarny, who previously founded Snyk. We’ve raised more than $100 million from world-class investors, including Index Ventures, Accel, GV and Boldstart. In 2025, Tessl ranked #2 in Sifted’s B2B SaaS Rising 100 and #20 in its AI 100. We’re building the context layer for AI coding agents and a platform for AI-native software development. As an early member of the team, you’ll help shape how we build and scale a company operating at the forefront of AI and software development. OVERVIEW OF THE ROLE We're hiring a Research Engineer to join our AI Research (AIR) team. You'll work on the components that make the outer loop real: how agent harnesses orchestrate model behaviour, how we evaluate what's actually working, how pipelines turn production traces into the next round of improvement, and how we diagnose the failure modes that matter to real users. These aren't four separate workstreams — they're parts of one system, and we want people who see them that way. We expect you to sit close to customers — joining calls, watching sessions, reading traces — and to let real workflows shape your research priorities. You'll have meaningful autonomy and the resources to run substantial experiments where the bar for success is shipped impact. You'll report to our AI Research Lead, and collaborate closely with engineering, product, and design. WHAT WE'RE LOOKING FOR We're explicitly building coverage across four skill areas. You don't need to be strong in all of them — but you should bring depth in at least one: - Agent harness and orchestration design — how tools, context, and control flow combine to make a useful agent. - Agentic eval methodology — task and repo-level evals, dataset curation, the craft of measuring what actually matters. - Outer-loop and pipeline thinking — feedback loops, training-data flywheels, bandit-style optimisation, anything that goes beyond a single agent session. - Failure-mode analysis — instrumenting agents, reading traces at volume, surfacing patterns engineering can act on. ESSENTIAL - 4+ years shipping AI/ML products in a startup or applied industry setting, with recent hands-on experience with LLMs and agentic systems. - Demonstrated depth in at least one of the four skill areas above. - Strong product and customer instincts: comfort joining customer calls, watching session recordings, and letting real workflows shape what you work on. - Sharp evaluation judgement: benchmarks where they exist, vibes and quick prototypes where they don't, and the taste to know which is appropriate. - Experience building datasets for evaluation or training, including the pipeline work that goes with it. - Deeply curious about agents and excited about reshaping how software is built. NICE TO HAVE - A Masters or PhD in a relevant computational field. - Direct experience with coding agents or code-generation systems. - Background in RL, bandits, or other outer-loop optimisation frameworks applied to LLMs. - Experience building synthetic data, dataset infrastructure, or internal tooling that other engineers actually used. - A project you can show us (GitHub links welcome) and a thoughtful answer to "Why Tessl?" WHAT YOU'LL DO No two weeks will look the same. A flavour: - Sit in on a customer session, understand how their agents are failing, design an eval that captures it, and drive a fix through to shipped improvement. - Close a piece of the outer loop end to end: production signal in, dataset out, eval scored, harness change shipped, metric moved. - Own a slice of our eval infrastructure: dataset curation, harness configuration, runner, analysis, and the comms back to engineering. - Prototype a new harness or context configuration and measure whether it actually moves the needle on real customer tasks. - Dig through pages of agent traces, build the tooling you need to make sense of them, and brief the team on what you found. - Partner with product and engineering on near-term shipping problems by bringing research rigour. - Pull a recent paper apart, work out what's actually transferable to our platform, and turn it into a concrete experiment. YOU’LL BE SUCCESSFUL IF… In your first 3 months, you might have shipped a new eval suite for a real customer workflow, improved an agent harness based on trace analysis, or built a pipeline that turns production failures into reusable test cases. SALARY AND BENEFITS Competitive salary commensurate with experience. Health insurance extending to partners and dependents, pension contributions, and the rest of what you'd expect. Our office is a couple of minutes from King's Cross — pet friendly, with regular team lunches, drinks, and socials. We're hybrid, with Monday, Tuesday, and Thursday as the primary in-office days. APPLICATION PROCESS - Intro call to understand "Why Tessl?" and to tell you a bit about us. - A call with our AI Research Lead to understand your ways of working and how you use agents. - A 4 hour technical take-home exercise extending our one-shot implementation. - A half-day on-site session including whiteboarding and hands-on activities. - Leadership chats with our Head of People, Head of Engineering and CEO. We care deeply about the warm, inclusive environment we’re building at Tessl and we value diversity – we welcome applications from those typically underrepresented in tech. If you like the sound of this role but are not totally sure whether you’re the right person, do apply anyway! LEARN HOW WE THINK AND WORK - On Tessl, The AI Native Development Startup https://www.tessl.io/blog/announcing-tessl-the-ai-native-development-startup - Announcing skills on Tessl: the package manager for agent skills https://tessl.io/blog/skills-are-software-and-they-need-a-lifecycle-introducing-skills-on-tessl/ - Podcast Episode: The End of Fragmented Agent Context, Guy Podjarny Tessl CEO https://youtu.be/ntkM-hRblfo
Staff Field Application Engineer
Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. We’re looking for a Field Application Engineer who’s wired for AI/ML, fluent in real-world problem-solving, and excited to build with the people actually using what we make. You will collaborate closely with the sales team and enterprise customers, leveraging your deep technical knowledge in AI to drive the adoption of our products and solutions. This is a customer-facing role that requires both technical expertise and excellent communication skills to convey complex technical concepts to non-technical stakeholders. This role is remote based out of North America with preference near one of our main hubs Santa Clara, CA; Boston, MA; or Toronto,ON. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are - You’ve lived in the AI/ML trenches, whether as a field engineer, a solutions architect, or the one tapped in when things needed to “just work.” - You speak both machine and human. Whether it’s a researcher or a skeptical executive, you know how to break things down and bring them to life. - You’re fired up about generative models, LLMs, and the edge of what’s possible when software meets purpose-built silicon. - Work directly with customers in meetings, presentations, and workshops to understand their technical challenges and provide effective solutions. - Partner closely with sales operations and product teams to provide feedback from customers and help shape future product enhancements. What We Need - Strong customer-facing skills with 5+ years of relevant technical, enterprise experience (such as Sales Engineer, Solutions Engineer, or Technical Account Manager). - Your field insight will help shape our roadmap, our features, and the way we show up for developers. - Proven expertise in AI technologies and frameworks, including machine learning, deep learning, natural language processing (NLP),computer vision, PyTorch or TensorFlow. - Experience with embedded systems, FPGA Architecture or AI accelerators. - Good understanding of data center, server architecture and deployment, thermal/power, and infrastructure management. What You Will Learn - How to explore and push the boundaries of AI performance on custom silicon and help developers do the same. - What it’s like to build the future from scratch, working alongside teams creating hardware, software, and everything in between. - To master the craft of fine-tuning LLMs, optimizing vision models, and building AI pipelines that run at scale. - An understanding of why field roles drive real impact turning on-the-ground insight into product breakthroughs and platform momentum. Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made. Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer. This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
Machine Learning Engineer, AI Models
Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. Join Tenstorrent’s AI Models team and work at the layer most ML engineers never see: bringing advanced models to life on custom AI hardware. You’ll own real workloads end‑to‑end including porting, tuning, and validating LLMs and vision models on our accelerator, and chasing down every last millisecond and percentage point of accuracy. This role is for people who love the craft of ML engineering and want their work to matter at silicon scale, not just behind another API. This role is hybrid , based in Cyprus. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are - Bring up, run, and debug modern ML models (e.g., transformers) using PyTorch or TensorFlow. - Analyze model behavior and performance, and identify bottlenecks across the stack. - Improve efficiency, correctness, and scalability of model execution in real systems. - Work closely with compiler, kernel, and hardware teams to drive performance and system-level improvements. - Help translate state-of-the-art model architectures into production-grade, high-performance deployments. What We Need - Strong experience building and working with ML models in PyTorch or TensorFlow. - Strong understanding of modern ML model architectures (ex: transformers). - Solid software engineering fundamentals with strong debugging and problem-solving skills. - Comfort working in a fast-moving, research-meets-engineering environment. - Bonus, not required: experience with profiling or performance tuning, or familiarity with quantization, flash attention, kernel fusion, memory hierarchies, C++, CUDA, or systems programming. What You Will Learn - How to bring state‑of‑the‑art LLMs and vision models to high performance on a custom AI accelerator. - How to trace and fix performance bottlenecks from PyTorch code down to kernels and memory systems. - How to turn research‑grade models into reliable, production deployments on new hardware. - The practical trade‑offs between techniques like quantization, FlashAttention, and kernel fusion when you’re optimizing real throughput, latency, and memory. - How your findings can drive changes across compiler, kernel, and hardware teams in a full‑stack co‑design loop Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer. This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
Field Sales Engineer
Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. We’re looking for a Field Sales Engineer who’s wired for AI/ML, fluent in real-world problem-solving, and excited to build with the people actually using what we make. You will collaborate closely with the sales team and enterprise customers, leveraging your deep technical knowledge in AI to drive the adoption of our products and solutions. This is a customer-facing role that requires both technical expertise and excellent communication skills to convey complex technical concepts to non-technical stakeholders. This role is remote based out of North America with preference near one of our main hubs Santa Clara, CA; Boston, MA; or Toronto,ON. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are - You’ve lived in the AI/ML trenches, whether as a field engineer, a solutions architect, or the one tapped in when things needed to “just work.” - You speak both machine and human. Whether it’s a researcher or a skeptical executive, you know how to break things down and bring them to life. - You’re fired up about generative models, LLMs, and the edge of what’s possible when software meets purpose-built silicon. - Work directly with customers in pre-sales meetings, presentations, and workshops to understand their technical challenges and provide effective solutions. - Partner closely with sales operations and product teams to provide feedback from customers and help shape future product enhancements. What We Need - Strong customer-facing skills with 5+ years of relevant technical, enterprise experience (such as Sales Engineer, Solutions Engineer, or Technical Account Manager). - Your field insight will help shape our roadmap, our features, and the way we show up for developers. - Proven expertise in AI technologies and frameworks, including machine learning, deep learning, natural language processing (NLP),computer vision, PyTorch or TensorFlow. - Experience with embedded systems, FPGA Architecture or AI accelerators. - Good understanding of data center, server architecture and deployment, thermal/power, and infrastructure management. What You Will Learn - How to explore and push the boundaries of AI performance on custom silicon and help developers do the same. - What it’s like to build the future from scratch, working alongside teams creating hardware, software, and everything in between. - To master the craft of fine-tuning LLMs, optimizing vision models, and building AI pipelines that run at scale. - An understanding of why field roles drive real impact turning on-the-ground insight into product breakthroughs and platform momentum. Compensation for all engineers at Tenstorrent ranges from $100k - $500k including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made. Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer. This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
Field Application Engineer, South East Asia
Tenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities. We are looking for a Field Application Engineer to serve as the technical bridge between Tenstorrent and customers across Southeast Asia. Based in Singapore, you will work closely with customers, partners, sales, and global engineering teams to understand AI and machine learning workloads, guide technical evaluations and deployments, troubleshoot issues across hardware and software, and help customers realize the performance of Tenstorrent’s AI platforms. This is a highly visible, customer-facing role that combines hands-on technical problem solving, solution development, and regional relationship building, with regular travel throughout Southeast Asia. This role is remote, based out of Singapore. We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting. Who You Are - A customer-focused technical professional who can build trust with application developers, engineering teams, and business stakeholders. - Comfortable translating complex AI/ML hardware and software concepts into clear recommendations for both technical and non-technical audiences. - A proactive and self-directed problem solver who can coordinate internal teams, external service providers, and customer stakeholders across South East Asia. - Willing and able to travel to customer sites for installations, maintenance, troubleshooting, demonstrations, and proof-of-concept delivery. What We Need - Experience as a Field Application Engineer, Application Engineer, Sales Engineer, Solutions Architect, or in a similar customer-facing technical role focused on AI/ML software and hardware. - Strong Python programming skills and deep familiarity with AI/ML frameworks such as PyTorch and TensorFlow; experience with Generative AI, LLMs, vision models, model deployment, training, or fine-tuning is preferred. - Experience designing scalable AI solutions and deploying applications in cloud environments, as well as physically installing, moving, and repairing servers, accelerator cards, and PCIe devices. - A Bachelor's degree or higher in Computer Science, Engineering, or a related field. Experience with C++, CUDA, parallel programming, or kernel development is a plus. - Languages proficiency: English, Mandarin, and Malay would be plus. What You Will Learn - How to architect and deploy innovative AI/ML solutions across a wide range of customer use cases, including Generative AI and LLM applications. - How to lead the full customer engagement lifecycle, from pre-sales technical discovery and demonstrations through proof-of-concept delivery and production deployment. - How to collaborate with product development, engineering, research, sales, and business development teams to influence the evolution of AI software and hardware solutions. - How to help establish regional service capabilities and support the growth of a Tenstorrent office in South East Asia. ***For This role, Singapore Citizens and Singapore Permanent Residents (PRs) is required. and visa sponsorship and work pass support are not available. Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer. This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
Senior Backend Engineer (ML)
COMPANY DESCRIPTION Today, when you go to your doctor and get referred to a specialist, your doctor sends out a referral and tells you, “They’ll be in touch soon.” So you wait. And wait. Sometimes days, weeks, or even months. Why? Because too often providers are overwhelmed with the painstakingly tedious work required to get paid by insurance companies. Powered by proprietary models, Tennr handles the complex paperwork that gets patients through the door and providers paid, helping operators get patients the right care, at the right time, in the right setting. ROLE DESCRIPTION We’re looking for a high-agency Senior Backend Engineer to bridge the gap between cutting-edge ML research and real-world product delivery. You’ll design and build agentic workflows that power complex healthcare processes to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare. This role involves creating data pipelines, evaluation harnesses, and scalable production-grade agentic systems. You’ll be responsible for researching the right tools and models, ensuring we always use the best-fit technology for each workflow.. As part of our ML team, you’ll work hand-in-hand with researchers to translate new techniques and insights into production-ready systems. The ideal candidate has full-stack engineering experience, hands-on expertise with LLM APIs, and a proven track record of deploying AI systems into production. You’ll work across the stack - from data collection to training pipelines to AI agent orchestration to frontend integration - bringing cutting-edge AI solutions to life in real-world healthcare environments. RESPONSIBILITIES As a Senior Backend Engineer on the ML team at Tennr, you’ll bridge the gap between cutting-edge ML research and real-world product delivery. You’ll help build agentic systems that use LLMs, and vision models, and structured automation to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare. - Ship full-stack AI systems end-to-end - Design resilient systems for model deployment, evaluation, and monitoring that stay reliable as traffic grows. - Go from ideation to code within hours; iterate quickly on experiments and data. - Collaborate with ML engineers, backend engineers, and cross-functional teams to integrate models cleanly with data pipelines and product QUALIFICATIONS - Proficiency in full-stack development, with a strong understanding of system design, backend engineering, and observability infrastructure. - A track record of working through the full lifecycle of building, testing, deploying, scaling, and monitoring LLM-centered software architectures. - Excellent communication and collaboration skills: aligning on interfaces, navigating tradeoffs, and driving cross-team execution. WHY TENNR? - Drive Impact: one of our company values is Cowboy, meaning you set the pace. You won’t just talk about things, you’ll get them done. And feel the impact. - Develop Operational Expertise: learn the inner workings of scaling systems, tools, and infrastructure - Innovate with Purpose: we’re not just doing this for fun (although we do have a lot of fun). At Tennr, you’ll join a high-caliber team maniacally focused on reducing patient delays across the U.S. healthcare system. - Build Relationships: collaborate and connect with like-minded, driven individuals in our Chelsea office 4 days/week - Free lunch! Plus a pantry full of snacks. BENEFITS - Beautiful new office at 345 Hudson Street - Unlimited PTO - 100% paid employee health benefit options - Employer-funded 401(k) match - Competitive parental leave
Senior AI Scientist, AI Products / Life Sciences
Passionate about precision medicine and advancing the healthcare industry? Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way. Tempus' proprietary platform connects an entire ecosystem of real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about the right treatments for the right patients, at the right time. At Tempus, we are pursuing a mission to redefine precision medicine through data-driven insights. The AI Products group is a multidisciplinary group of AI scientists and engineers focused on developing AI-driven precision medicine and therapeutic solutions across the clinical and drug development lifecycle. As a Senior AI Scientist, you will operate at the intersection of machine learning and clinical science. You will research, design, and deploy cutting-edge multimodal AI models that leverage one of the world's largest clinical and molecular datasets—transforming patient care at a massive scale. Key Responsibilities Technical Contribution: This is a high-impact individual contributor role, while significant hands-on contribution is expected. Algorithmic Innovation: Design and train state-of-the-art models utilizing multi-modal data, including but not limited to pathology slides (WSI), genomic profiles (DNA/RNA), spatial omics, and longitudinal patient records. Collaboration with Life Sciences Companies: Develop sophisticated AI solutions to support biomarker discovery, therapy outcome prediction, and the advancement of Companion Diagnostics (CDx). Regulatory Alignment: Ensure all AI products meet stringent regulatory guidelines and quality standards for healthcare applications. Scientific Contribution: Maintain Tempus’s position as a thought leader by publishing original research in high-impact venues and engaging with the global AI research community. Qualifications We value diverse perspectives and recognize that outstanding talent doesn't always follow a linear path. If you are passionate about this work and believe you have what it takes to thrive here, please apply even if you do not precisely match all the qualifications below. Education: Ph.D. in Computer Science, or a related quantitative field. Professional Experience: 1+ years of industry experience leading end-to-end development of machine learning models, from research and experimentation to validation and production deployment. Domain Expertise: Demonstrated expertise in large-scale deep learning and training production-grade models is required. Experience in computer vision and multimodal modeling (pathology, multi-omics, etc.) is preferred. Prior work in medical or healthcare AI is highly valued but not required. Research Excellence: A strong track record of peer-reviewed publications in leading machine learning or computer vision venues. Publications in medical or healthcare AI venues are preferred. Engineering Excellence : Proficiency in Python and the modern deep learning stack (e.g. Pytorch). Strong product-level coding skills for model development are essential, along with a solid understanding of industry-standard software engineering and development practices. CHI: $140,000-$200,000 NYC/SF: $150,000-$210,000 Remote - USA Range - $140,000-$200,000 The expected salary range above is applicable if the role is performed from California and may vary for other locations (Colorado, Illinois, New York). Actual salary may vary based on qualifications and experience. Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position. Additionally, for remote roles open to individuals in unincorporated Los Angeles – including remote roles- Tempus reasonably believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: engaging positively with customers and other employees; accessing confidential information, including intellectual property, trade secrets, and protected health information; and appropriately handling such information in accordance with legal and ethical standards. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
AI Researcher (Multimodal Audio/Video Generation)
ABOUT US Tavus https://www.tavus.io/ is a research lab pioneering human computing. We’re building AI Humans: a new interface that closes the gap between people and machines, free from the friction of today’s systems. Our real-time human simulation models let machines see, hear, respond, and even look real—enabling meaningful, face-to-face conversations. AI Humans combine the emotional intelligence of humans with the reach and reliability of machines, making them capable, trusted agents available 24/7, in every language, on our terms. Imagine a therapist anyone can afford. A personal trainer that adapts to your schedule. A fleet of medical assistants that can give every patient the attention they need. With Tavus, individuals, enterprises, and developers can all build AI Humans to connect, understand, and act with empathy at scale. We’re a Series A company backed by world-class investors including Sequoia Capital, Y Combinator, and Scale Venture Partners. Be part of shaping a future where humans and machines truly understand each other. THE ROLE We’re hiring a Senior AI Researcher to lead research in audio-visual avatar generation. This role is for someone who thrives in ambiguity, has a track record of pushing generative models to new frontiers, and wants to define what human–AI interaction looks like in practice. YOUR MISSION 🚀 - Lead research efforts on audio-visual generation for avatars (Neural Avatars, Talking-Heads), with a focus on conversational settings. - Design models that are coupled with conversation flow — capturing and generating verbal + non-verbal signals in sync. - Drive innovation in diffusion models, long-video generation, and audio-visual modeling. - Translate research into production by partnering with Applied ML and engineering. - Mentor researchers, set research directions, and publish impactful work. YOU’LL BRING: - A PhD or equivalent research experience, plus 2–3+ years of hands-on experience applying generative models at scale. - Expertise in diffusion models and awareness of the latest efficiency techniques. - Experience in multimodal generation — spanning video, audio, and language. - Proven innovation in long-video generation and/or audio generation. - Excellent programming skills — fluent in PyTorch and GPU-optimized workflows. - Track record of publications in top-tier venues (CVPR, NeurIPS, BMVC, ICASSP, etc.). - Experience leading research activities or mentoring teams. NICE-TO-HAVES: - Skills in 3D graphics, Gaussian splatting, or large-scale training setups. - Broad exposure to generative AI models beyond your specialty. - Familiarity with software development best practices. LOCATION: Preferred: San Francisco (hybrid) or London. Remote within U.S. or Europe considered for exceptional candidates.
Research Engineer in Data
Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. ABOUT THE ROLE The Data team manages the complete lifecycle of data for researchers - from sourcing and large-scale processing to delivering datasets that power our models. Data sits at the heart of our Research efforts and enables all other teams. As part of the Data team, you’ll work with over a million hours of video and audio data. This role exists at the intersection of applied research, data engineering, and ML infrastructure rather than being a traditional research position. You’ll build the world’s best human-centric data lake by collaborating closely with our model training teams. By understanding their requirements, you’ll extract new features and annotations that elevate our datasets. You should be passionate about enhancing model performance through high-quality, accurate datasets. Our infrastructure and pipelines are in great shape, and this role provides room to not only enhance them but also influence the team’s longer-term strategy. WHAT WE'RE LOOKING FOR: - A strong background in data-centric, applied Machine Learning, with hands-on experience improving model performance through data quality, curation, labeling, and evaluation rather than model architecture alone - Experience working on the data layer of Generative AI products, particularly involving images, video, or audio - Excellent Python skills, with a strong focus on writing clean, maintainable, and well-tested code - Nice to have if you have experience designing, building, and operating workflow orchestration systems and large-scale data processing pipelines WHY JOIN US? We’re living the golden age of AI. The next decade will yield the next iconic companies, and we dare to say we have what it takes to become one. Here’s why, OUR CULTURE At Synthesia we’re passionate about building, not talking, planning or politicising. We strive to hire the smartest, kindest and most unrelenting people and let them do their best work without distractions. Our work principles serve as our charter for how we make decisions, give feedback and structure our work to empower everyone to go as fast as possible. You can find out more about these principles here. https://www.notion.so/How-we-work-at-Synthesia-f794caa72f8446efb6be22b551ce0fa2?pvs=21 SERVING 50,000+ CUSTOMERS (AND 50% OF THE FORTUNE 500) We’re trusted by leading brands such as Heineken, Zoom, Xerox, McDonald’s and more. Read stories from happy customers https://www.synthesia.io/case-studies and what 1,200+ people say on G2 https://www.g2.com/products/synthesia/reviews#reviews. PROPRIETARY AI TECHNOLOGY Since 2017, we’ve been pioneering advancements in Generative AI. Our AI technology is built in-house, by a team of world-class AI researchers and engineers. Learn more about our AI Research Lab and the team behind. https://www.synthesia.io/research AI SAFETY, ETHICS AND SECURITY AI safety, ethics, and security are fundamental to our mission. While the full scope of Artificial Intelligence's impact on our society is still unfolding, our position is clear: People first. Always. Learn more about our commitments to AI Ethics, Safety & Security https://www.synthesia.io/ethics. THE GOOD STUFF... - Competitive compensation (salary + stock options + bonus) - Hybrid work setting with an office in London, Amsterdam, Zurich, Munich, or remote in Europe. - 25 days of annual leave + public holidays - Great company culture with the option to join regular planning and socials at our hubs - + other benefits depending on your location #LI-MD1
Principal ML Platform Engineer
Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. We’re looking for a Principal Engineer to join the ML Platform team at Synthesia. Our team builds and operates the systems that allow researchers and product teams to train, serve, and deploy generative models reliably and efficiently. This includes research infrastructure, production serving systems, internal tooling, and the platform interfaces that connect them. A growing part of our mission is making these systems more automation-friendly and agent-oriented, so that workflows can increasingly be operated through reliable tooling rather than manual effort. We’re looking for a strong generalist with a systems mindset: - someone who is comfortable working across infrastructure, backend systems, and tooling, and who has seen ML systems in practice. - this is not a pure ML Engineer role. We’re especially interested in people who think deeply about reliability, scalability, performance, and resource efficiency in complex production environments. This is a hands-on IC role with significant ownership. You’ll help shape how our ML platform evolves as we scale the number of models, workloads, tools and teams relying on it. WHAT YOU’LL DO - Design and improve the platform systems that support model training, evaluation, and production serving. - Build infrastructure and tooling that make ML workloads more reliable, scalable, and cost-efficient. - Develop internal tools and workflows that are easy to operate both by humans and by agents. - Work on the architecture behind how models are deployed, served, and operated across research and product environments. - Improve how we schedule, monitor, and debug workloads running on GPUs and cloud infrastructure. - Develop internal tools and abstractions and agentic systems that reduce operational overhead for researchers and engineers. - Drive improvements across observability, automation, reliability, and developer experience. - Collaborate closely with researchers and product engineers to understand pain points and turn them into robust platform capabilities. - Contribute to technical direction and make pragmatic architectural tradeoffs as the platform grows. YOU’LL THRIVE IN THIS ROLE IF YOU HAVE - Strong experience building or operating production systems with a focus on reliability, scalability, and maintainability. - A systems mindset: you naturally think in terms of bottlenecks, failure modes, interfaces, resource usage, and long-term operability. - Solid hands-on experience with cloud infrastructure, Linux, and infrastructure automation. - Experience with Kubernetes and operating distributed workloads in production. - Strong coding skills, ideally in Python or similar languages used for backend systems and tooling. - Strong judgment around where automation adds leverage, and where human control and reliability matter most. - Experience building internal platforms, developer tooling, or infrastructure abstractions used by other engineers. - Comfort working in ambiguous environments and taking ownership of open-ended technical problems. - A pragmatic approach: you care about solving the right problem well, not over-engineering. PARTICULARLY RELEVANT EXPERIENCE - Operating ML infrastructure or model serving systems in production. - Supporting research or data-intensive workloads. - Working with GPU-based systems or other performance-sensitive infrastructure. - Experience with observability and debugging in distributed systems. - Familiarity with Terraform, Datadog, GitHub Actions, or similar tools. BONUS POINTS FOR - Experience building agentic or LLM-powered internal tools. - Experience with workflow orchestration systems such as Temporal. - Experience working at the boundary between research and production engineering. - Familiarity with performance optimization, scheduling, or resource allocation problems. - Experience building lightweight product or developer-facing tools.
Principal Applied Research Engineer
Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. THE OPPORTUNITY At Synthesia we really care about video generation, especially about human centric avatar video generation. This led us to release models such as EXPRESS-Video https://synthesiaresearch.github.io/express-video/, and soon our latest video model - these are the best avatar video models in the world, and we are committed to continuing and double down our efforts in leading that area. Our goal is to get to human centric video models that can generate arbitrary long videos at high resolution with arbitrary actions and events. That means continuously training large generative video models from scratch with the proprietary data pipelines and compute infrastructure to support it at scale. We are looking for a technical leader who owns the full stack end-to-end, someone who bridges pre-training and post-training, sets long-term direction alongside research leadership, and is personally present at the hardest parts of the work. If building foundation model capability from the ground up at a company genuinely committed to leading the field sounds like the right next challenge, this role was written for you. ABOUT THE ROLE Synthesia's video generation capability is core to everything we ship. It involves roughly 15 people working daily across pre-training and post-training stages, and the complexity of coordinating across those stages, at the scale of compute and data we now operate, requires a different kind of technical leadership. We're looking for a Principal Research Engineer (L7) to own the full technical stack for offline video generation. This is a senior individual contributor position with outsized scope and influence. You'll partner directly with research leadership and team leads to define long-term strategy, resolve the hardest cross-cutting technical problems, and raise the bar for how quickly research reaches product. The person we're looking for has trained large generative models from scratch, not supervised it from a distance, but done it, debugged it, and shipped it. More than that, they're driven by a genuine ambition to push what's possible in video generation, and they care deeply about seeing that work land in product and reach users. What you'll do - Own the end-to-end technical direction for offline video generation, spanning pre-training and post-training, resolving the artificial boundary between those two stages in service of shipping better models faster. - Partner with research leadership and team leads to define a unified long-term roadmap, broken into achievable objectives, and drive execution against it. - Identify the most critical technical gaps across the video generation pipeline and jump in to unblock them, whether that means architectural decisions, training stability, post-training alignment, or cross-team coordination. - Increase the velocity at which research ships to product: accelerate problem-solving, improve research-to-production handoffs, and increase visibility of research output in partnership with PMs. - Coach and elevate more junior researchers and engineers toward senior technical thinking and execution. - Help shape team structure and refine processes to enable high-velocity, cohesive execution across research. You'll thrive in this role if you have - A proven track record training large-scale video generation models from scratch, across multiple nodes, at the scale of millions of hours of data. - Deep experience with post-training techniques at scale: RLHF, GRPO, DPO, and the judgment to know when and how to apply them. - A proven track record data quality and you are not reluctant to question it. - The ability to think strategically about multi-year research direction and execute hands-on at the frontier of what the team is building. - Strong cross-functional influence: you shape how teams work together without needing positional authority to do it. - A leadership style grounded in technical involvement. You lead by example, inspire through craft, and communicate with clarity. - Genuine hunger to unlock new capabilities and an obsession with shipping. You're not satisfied by research that stays in a notebook. You want it in the hands of users. Particularly relevant experience - Having owned a major model generation or capability jump end-to-end, from training runs through to product deployment. - Working across both pre-training and post-training stages on the same model family, with direct accountability for the outcomes of both. - Experience operating at scale: large distributed training runs, significant compute budgets, and multi-million hour data pipelines. - Applying alignment and fine-tuning techniques in a video or multimodal context, not just text. - Experience with human feedback pipelines applied to generative video or audio. - Leading or significantly influencing the technical direction of a research team while remaining hands-on. Dealbreakers We will not be a good fit if you prefer to lead without staying technically involved, or if clear and direct communication across research and product isn't one of your strengths. This role requires presence at the frontier of the work, not above it. And if shipping doesn't excite you as much as the research itself, this probably isn't the right role.
Staff Software Engineer
We are looking for an exceptional Staff Software Engineer to own the most visible surfaces in the company: the AI agent that programs industrial robots through plain conversation, and the vertical applications built around it. This role tackles one of the most ambiguous, high-leverage challenges in applied AI: turning a successful LLM-powered demo into a product customers rely on. Our verticals are the real jobs customers hire robots to do, like palletizing, welding, and machine tending. Each vertical is a product surface, with purpose-built features and user experiences, designed from the start for the agent to drive. Your impact will be measured not just by the code you write, but by your ability to make the agent's quality measurable, harden it to release, turn our verticals into products customers love, and raise the technical bar of a small, founding-stage team working directly with our founder, product leadership, and first customers. WHAT YOU'LL DO 🏗️ STRATEGIC ARCHITECTURE & LONG-TERM VISION - Define the Future: Own the long-term product and technical vision for the AI agent and the vertical applications it powers, anticipating the scale, security, and product needs of LLM-powered robot programming a year or more out, including its core representation problem: how a language model safely reads, writes, and edits large structured automation programs. - Design the Experience: Own how programming a robot through conversation should feel: the interactions, defaults, and guardrails that earn an operator's trust. - Decouple and Scale: Design the agent's tool surface as clear, stable interfaces against our robot platform APIs, so that what the agent can do grows with the platform, not against it. - Standard Setter: Set the engineering standards for a nondeterministic product: latency and token budgets as product requirements, and evaluation gates as the bar every release must clear. 🏎️ COMPLEX PROJECT LEADERSHIP & EXECUTION - Product Ownership: Act as part-PM for your surface: work backwards from the product experience, spend real time with customers and operators, be opinionated on the roadmap, and own outcomes, not tickets. - Build the Verticals: Ship vertical applications alongside the agent: purpose-built flows for palletizing, welding, and machine tending that encode how the work is really done, and that the agent can drive end-to-end. - Drive the Program: Lead the agent's path from demo to release across our robot platform, QA, product, and AI teams: convert fast-moving asks into testable requirements and hold scope against a real ship date. - Hands-on Delivery: Own production hardening end-to-end across the agent's TypeScript/Node stack: reliability, secure key management and usage controls, degraded/offline modes, and review- and QA-gated release engineering. 🛠️ PRODUCTION EXCELLENCE & ORGANIZATIONAL LEVERAGE - Systemic Quality: Build the agent's evaluation discipline (datasets mined from real usage, regression gates in CI, model-graded scoring) so that every early-customer failure, in any vertical, becomes an eval case and every eval win becomes a release decision. - Technical Multiplier: Mentor and coach the engineers around you, significantly contributing to their technical growth and to the quality of everything the team ships. - Influence & Accountability: Bring rigor to how the company understands agent quality: shared metrics that leadership, product, and customers can trust. WHO YOU ARE We are looking for a product-minded, LLM-native engineer who has shipped AI to real users and gets excited about both the agent and the real-world automation tasks it will drive, like palletizing and welding. You want to understand how the work actually gets done on a factory floor, and you build for the people doing it. This is not a model-training research role, and no robotics background is required. Agentic coding tools should already be part of your default workflow. SKILLS YOU'LL BRING - Experience: 8+ years of professional software engineering, including hands-on AI work: shipping an LLM-powered product (agent, copilot, AI-native workflow), building model evaluation systems, or production data work for AI/ML. Ownership of an LLM product end-to-end is the strongest fit. - LLM Product Engineering: Fluency in agent loops and tool calling, prompt and context management, structured output, model selection and fallbacks, and token/latency economics as engineering constraints. - Evaluation & Quality: You've built eval datasets, harnesses, or regression gates for nondeterministic systems and used them to make real launch decisions. - Product Thinking: You've owned what to build, not just how to build it: you talk directly to users, form sharp opinions about their problems, and put decisions in writing. Experience serving operational, domain-heavy users (manufacturing, logistics, field operations) is a strong plus. - Design Sensibility: Strong opinions about user experience, held loosely: you prototype interactions before hardening them, sweat the details users actually feel, and partner naturally with design. - Excelling in Ambitious Environments: Your best work happened where the bar was public, leadership was close to the product, and the plan changed weekly. - Tech Stack Expertise: Fluency in TypeScript/Node.js is preferred, with range across our core stack (React, Python, C++, Kubernetes). 📍 LOCATION AND BENEFITS This role is hybrid with 3 days/week onsite in our NYC office, centrally located near Grand Central Station. OFFICE & WORKPLACE PERKS - Direct Access to Robotics: Work alongside our physical products with dedicated demo and testing cells, allowing you to validate your work on real robots daily. - Collaborative & Modern Space: Enjoy open collaborative spaces for team problem-solving and focus zones for deep work. - Customizable Workstations: We provide ergonomic and customizable setups to ensure a comfortable and productive work environment. - Amenities: Daily catered lunch, fully stocked kitchens with snacks and beverages, and regular team events. COMPENSATION The base salary range for this role is $215,000 to $250,000, depending on experience. Base salary is just one part of the overall compensation at Standard Bots. All Full-Time Employees are eligible for Employee Stock Options. We also offer a package of benefits including paid time off, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) to regular full-time employees.
Senior Software Engineer, AI Team
About Standard Bots Standard Bot's mission is to significantly lower the barrier to entry to real-world automation, bringing the power of bits to the world of atoms and unlocking productivity for entire new industries and users. Our user-facing applications and APIs directly empower individuals and developers to jump directly into solving their challenges without the need for a background in robotics or automation. A relentless focus on simplicity and ease of use brings this revolutionary potential to new users and industries. What You'll Do We're looking for a seasoned, broad software engineer to join our AI team and build the systems our models depend on. Our AI researchers push the modeling frontier; this role owns nearly everything around it — the data that goes in, the infrastructure that trains and evaluates, the perception plumbing that connects cameras to the robot, and the product surfaces that put all of it in a customer's hands. The AI team is small, so the surface area is wide and the ownership is real. You may move between adding functionality to the data ingestion pipeline, a camera calibration bug, and a customer-facing feature in the same month — and you'll decide which of those matters most this week. Location and Benefits This role is hybrid with 3 days/week onsite in our NYC office, centrally located near Grand Central Station. The office is fully equipped with robots for demos and testing! Responsibilities - Identify, scope, and drive high impact technical and user facing projects across the AI team's stack - Ship the product surfaces that expose AI capabilities to users — the applications and APIs customers actually touch - Mentor and advise other engineers as well as peers on technical and personal advancement - Drive technical decision making and implementation through hands-on and technically deep work Plus ownership of one or more of these, depending on where your experience is strongest: - Data pipelines: the flywheel for end-to-end AI — ingestion of teleop and demonstration data, labeling and curation tooling, dataset quality metrics - Training and evaluation infrastructure: training pipelines, experiment tracking, GPU orchestration, model versioning, and deployment out to robots - Perception integration: camera calibration, vision pipelines, and getting model outputs into the robot runtime reliably Tech Stack - Front-end: React, TypeScript - Back-end: Node.js, Python, C++ - ML: PyTorch - Kubernetes - Docker - GitHub Actions Who You Are We do not require a background in robotics. We're looking for engineers who are self driven, optimistic, creative, and resourceful. We have no requirements for a background in robotics, but find most success with engineers excited by the domain and looking to apply their expertise and experience in a new area. Our ideal candidate will be open to doing approximately 70% back-end and 30% front-end work with flexibility to sway more towards one or the other, depending on current projects and team needs. This is a generalist role first — but on the AI team, so we're looking for someone whose breadth already reaches into at least one AI-adjacent domain. Skills You'll Bring - Experience: You have a strong technical background, including 5-10 years of experience working in complex environments - AI-adjacent depth (strongly preferred): You have professional experience in at least one of ML Ops, data pipelines, or computer vision — for example building training or inference infrastructure, running large-scale data ingestion and curation, or shipping a vision pipeline to production. You don't need to have trained the models yourself. - Growth + Optimism: You are excited by new domains and challenges, and eager to pick up new context to accelerate your impact - Clarity of Communication: You excel in written and verbal communication and enjoy leveraging your skills to support your peers - Impact Driven: You relentlessly seek user impact and value execution over technical idealism - Proactive Simplicity: You prioritize simplicity, reliability, and velocity and proactively identify opportunities to improve proposed implementations or incumbent systems - Excelling in Ambitious Environments: You excel in dynamic and ambitious environments, working with a diverse group of individuals and backgrounds Compensation and Benefits The base salary range for this role is $180,000 to $210,000, depending on experience. Base salary is just one part of the overall compensation at Standard Bots. All Full-Time Employees are eligible for Employee Stock Options. We also offer a package of benefits including paid time off, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) to regular full-time employees.
Senior 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 Senior Engineer on the AI team, you’ll build and improve the core coding AI agents that power Bolt.new. You’ll collaborate directly with product, design, engineering, and data to solve key agent challenges: evaluation harnesses, context compression, maintaining context across large codebases, orchestrating intuitive multi-step workflows, and tackling everything from simple UI tweaks to complex architectural decisions. This isn't API integration work. You'll be contributing to how AI reasons about and generates full-stack applications, working alongside experienced engineers in a problem space where the patterns are still being invented. Your work ships to millions of users building real products with Bolt.new every day. 🛠️ How You'll Contribute - Build and ship state-of-the-art coding agents for Bolt.new (long-running workflows, context compression, agent loops, model routing). - Own agent capabilities end-to-end: planning, tool use, code generation/editing, and maintaining context across large codebases and multi-turn sessions. - Integrate and evaluate multiple LLM providers (OpenAI, Anthropic, Google): benchmark quality/cost/latency; choose the right model for each task; and adopt new model features. - Design safe, reliable tool-use and workflow orchestration (e.g., Vercel AI SDK, LangGraph): structured actions, guardrails, and production observability. - Partner closely with product/design/engineering/data to deliver robust AI features; mentor and raise the bar for peers. - Build and maintain evaluation harnesses and datasets; analyze failure modes and user feedback to continuously improve quality and reliability. 💡 Qualifications - LLM Experience: Have shipped LLM agents to production environments used by thousands of users. - Prompt Engineering: Deep understanding of prompt engineering best practices to guide LLM behavior. Able to craft, refine, and optimize prompts for different tasks and models. - Software Engineering Skills: Solid software engineering fundamentals with experience in building production-ready systems. - Autonomous Execution : Ability to deliver high-quality work with minimal oversight. Comfortable owning tasks end-to-end, managing complexity, and driving projects to completion within your functional area. - Problem-Solving : Strong analytical and problem-solving skills with the ability to debug complex AI behaviors and bring clarity to ambiguous tasks. - Data-Driven Mindset : Comfortable collecting, curating, and analyzing data to inform decisions. Able to build datasets, identify patterns in agent behavior, and translate findings into actionable 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 - 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. 📌 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
Senior Machine Learning Engineer
We are hiring a Senior Machine Learning Engineer to contribute to cutting-edge Research & Development. As innovators in speech technology, our mission is to Understand Every Voice - a vision that has propelled us to be world leaders of Voice AI. Fuelled by innovation, inclusivity, and a passion for making a global impact through world-leading Speech AI, we're looking for an experienced Senior Machine Learning Engineer to accelerate our efforts towards exceptional speech solutions. Our Modelling Team trains diverse models, including large self-supervised ones, supporting Speechmatics towards being the most accurate speech recognition system globally. It also ensures their deployment into production, working with the latest developments in ML, but also with the best engineering practices for software engineering and model serving. What you’ll be doing: - Work on complex R&D projects, with a diverse group of engineers, to achieve ambitious goals. - Stay current with the latest developments in Machine learning. - Execute high-impact projects to ensure team success. - Work collaboratively across departments and mentor team members (where appropriate). Who we are looking for: - Demonstrated experience in collaboratively pursuing ambitious R&D agendas. - Precise attention to detail, but is also aware of how the big picture relates to company goals, and where the field of Machine Learning is headed towards. - Understanding of the modern Machine Learning stack, for example: - Knowledge of contemporary transformer architectures (e.g., GQA, KV-caching) and best practices. - Experience in distributed training techniques. - Familiarity with optimisation strategies for model inference (e.g., dynamic batching, flash attention, speculative decoding). - With preferred backgrounds covering some of the following: - Publications in top-tier conferences. - Contributions to popular open-source repositories. - Technical writing skills as evidenced by relevant publications or blogs. We encourage you to apply even if you do not feel you match all of the requirements exactly. The list of requirements is intended to show the kinds of experience and qualities we’re looking for, but it is not exhaustive. If you are interested in the role, the team, and our mission, we would love to consider your application. We are always open to conversations and look forward to hearing from you. Who we are: Speechmatics is the leading expert in Speech Intelligence, and uses AI and Machine Learning to unlock business value in human speech worldwide. We work with an amazing mix of global companies, and our technology can integrate into our customers stack irrespective of their industry or use case – making it the go-to solution to harness useful information from speech. Joining us means working with some of the smartest minds around the world, focused on cutting-edge projects and deploying the latest techniques to disrupt the market. We believe in putting people first; we’ll do all we can to help you develop your skills and give you the tools you need to thrive. Our Focus Fridays give you an undisturbed day of focus, offset with Together Tuesdays when we have our team meetings, so you've always got the right balance. We have structured a hybrid approach that includes 2-3 designated office days each week. This arrangement ensures that while we embrace the advantages of remote work, we also maintain the vital connection and synergy that only in-person interactions can foster. This is only the beginning; we’re looking for amazing people like you to continue our journey… What we can offer you: No matter what stage of your career you’re at - from paid internships and first-job opportunities through to management and senior positions - we’ll support you with the training and development needed to reach your career aspirations with us. There really is no shortage of opportunities here for you to get involved and collaborate with those around you to deliver your best work. We offer incredibly flexible working, regular company lunches, and birthday celebrations. But that’s not all. We’ve spoken to our teams to find out what they want. From Private Medical, and Dental for you and your family, through to global working opportunities, a generous holiday allowance and pension/401K matching, we want to make sure our employees and their families are looked after. Every employee will receive a working from home allowance for tech or home office equipment (on top of your choice of laptop and accessories of course). Our approach to parental leave is designed to support employees globally. While this varies by geo, we have support in place for parents (including adoption assistance and reproductive health services) to ensure they have the time and financial resources needed to care for their growing families. At Speechmatics, our mission is simple: Understand Every Voice out there. That's not just about our tech – it's the heart and soul of who we are. We welcome different experiences, viewpoints, and identities. For us, it’s not just the right thing to do; it’s our catalyst for sparking innovation and creativity. Our teams thrive in an environment that celebrates and supports everyone – no matter their gender, identity or expression, race, disability, age, sexual orientation, religion, belief, marital status, national origin, veteran status, pregnancy, or maternity status. But we don’t just open the door to diversity – we actively welcome it. Why? Because we believe every unique voice adds something special to our team, leading us to smarter solutions and a better workplace. So, come as you are and join our Speechling community. We’re building a place where every voice not only gets heard but is also respected and valued. For more information on us , please visit our website and follow Speechmatics on our social channels via Twitter , Facebook , LinkedIn , and YouTube. We rely on legitimate interest as a legal basis for processing personal information under the GDPR for purposes of recruitment and applications for employment. LI-Hybrid
Machine Learning Engineer, Voice
ABOUT US Our mission is to reinvent the way people learn, starting with language. Learning a language can change a life by opening doors to new cultures, careers, and communities. Two billion people around the world are actively trying to learn a language, but the best way to learn (one-on-one tutoring) is hard to access at scale and hasn’t been meaningfully improved in decades. Speak is building a human-level, AI-powered tutor in your pocket: a conversation-first experience that lets learners actually speak, get instant feedback, and progress through carefully designed lessons. The result is a complete path from beginner to confident speaker across multiple languages. Speak first launched in South Korea in 2019, where Speak has now become the number one language learning app, and we now serve learners across many markets and 15+ languages. Speak is one of the world’s leading AI companies, with over $150m raised in venture investment from OpenAI, Accel, Founders Fund, Khosla Ventures, and more, with a distributed team across San Francisco, Seoul, Tokyo, Taipei, and Ljubljana. ABOUT THIS ROLE We are looking for an experienced Machine Learning Engineer to join our team and help develop cutting-edge speech recognition models that help teach language fluency. In this role you will take ownership of the end-to-end modeling pipeline, from training and experimentation to deployment and monitoring. You will also work closely with Product teams to design innovative learning experiences and measure the efficacy of production models as they affect our end users. We are a small, dynamic team where you will contribute as a developer and thought partner on team projects like ASR, assessment, pronunciation, content personalization, and much more. This is an incredibly exciting time to join an ML team designing a personalized learning experience that will revolutionize language learning for millions of learners worldwide — come join us! WHAT YOU'LL BE DOING - Training and deploying ASR models end-to-end, including monitoring, performance tracking, and retraining - Improving the pronunciation model that provides precise feedback, and make it more central to our learning app - Creating metrics to measure ASR performance across tasks and languages - Expanding our ASR systems to new languages and markets - Building and maintaining data infrastructure such as training/evaluation datasets and labeling pipelines WHAT WE'RE LOOKING FOR - Extensive experience training large models on GPUs and deploying custom deep learning models - Proficiency in Python and common Deep Learning frameworks like PyTorch - Demonstrated experience owning ML pipelines end to end, from POC to production - Strong communication skills and the ability to explain complex ML concepts to non-technical stakeholders - Sharp product sense and an ability to think broadly and cross-functionally about model quality in the context of user experience - Bonus - Experience with speech or audio OFFICE - San Francisco, CA WHY WORK AT SPEAK 1. Join a fantastic, tight-knit team at the right time: we're growing very quickly, we've most recently raised our Series C from some of the top investors in the valley, and we've achieved product-market fit in our initial markets. You'd join at a magical time when a single person could significantly change the course of the company. 2. Do your life's work with people you’ll love working with: we care strongly about our craft and want every person at Speak to feel like they're growing every day. We believe in the idea that working with people you both enjoy and have respect for makes everything better. We hire thoughtfully and only work with people we admire deeply. 3. Global in nature: We're live in over 40 countries and launching in a number of new markets soon. We have dedicated offices in San Francisco, Ljubljana, Seoul, Tokyo, and Taipei, and you’ll have the opportunity to talk to users in each of these regions on a regular basis as well as travel. 4. Impact people's lives in a major way: Learning a language is one of the single most life-changing skills one can learn, and right now 99% of people never achieve their goal because the process is broken. We’re helping millions of people achieve their goals and improve their lives. Speak does not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
Deep Learning Manipulation Engineer
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 a Deep Learning Engineer to develop and refine models for robotic manipulation. You will design, train, and deploy algorithms that enable robots to interact intelligently and adapt to their environments. Working closely with our machine learning, robotics, and research teams, you’ll ensure these models are robust, efficient, and ready for real-world challenges. Your work will directly advance Skild AI’s robotics capabilities, enabling robots to perform complex tasks autonomously. Responsibilities - Design, implement, and optimize deep learning models for robotic manipulation. - Model robotic manipulation processes to enable analysis, simulation, planning, and controls - Collaborate with cross-functional teams to develop scalable and generalizable manipulation solutions for deployment in robotic systems. - Work with inference, deployment, and application teams to integrate deep learning models seamlessly into robotic platforms. Preferred Qualifications - BS, MS, or higher degree in Computer Science, Robotics, Mechanical Engineering, or a related field, or equivalent practical experience. - Proficiency in Python and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc. - Proficiency with advanced deep learning techniques and architectures as well as reinforcement learning and or imitation learning. - Experience with distributed deep learning systems. Base Salary Range $100,000 - $300,000 USD
Strategist, Agent Development (Swedish speaking)
ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/. Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. WHAT YOU'LL DO As an Agent Strategist https://sierra.ai/blog/agent-strategist-your-phd-in-applied-ai, you own how Sierra's AI agents come to life for our customers — from first pilot to a production agent handling thousands of conversations a day. You sit at the intersection of product, building agents and customer-facing work, and stay just as close to the technology as you are to the people you build for. You transform a customer's thorniest, highest-stakes problems into agents that measurably move their business. - Own the Agent Development Life Cycle https://sierra.ai/blog/agent-development-life-cycle. Take agents from initial scope through conversation design, tooling, and evaluation, to launch and continuous iteration in production — translating business goals into an agent that outperforms, and tuning relentlessly until it's the best version of itself in the real world. You'll have real ownership and autonomy over the outcome. - Be the trusted strategic advisor to enterprise leaders. Sit shoulder-to-shoulder with our customers' executives, understand what success actually means to their business, and shape the AI strategy that gets them there. - Partner with the customer's working teams day to day. Run working sessions, troubleshoot the agent as issues come up, and make the calls that keep the build on track for launch. - Directly impact product. Surface unmet needs from your customer work, and feed them back to research and platform to shape where Sierra goes next. EXAMPLE PROJECTS These are the kinds of projects that strategists on our team have worked on recently: - Scope and launch a subscription-retention agent for a large telecommunications or media company that consistently outperforms human agents at saving at-risk customers. - Stand up an agent for a commerce customer that troubleshoots a broken device end-to-end and personalizes product recommendations in the same conversation. - Redesign the conversation flows for a financial services agent so it can handle complex, regulated interactions — and convert a pilot into a multi-team production deployment. See our financial services blog https://sierra.ai/blog/five-ways-to-use-ai-agents-financial-services for examples. - Partner with product on a net-new capability — a new agent architecture or self-service tooling — using a live customer as the design partner. - Define the evaluation and quality bar for a new agent, then use those metrics to drive a measurable lift in outcome https://sierra.ai/blog/outcome-based-pricing-for-ai-agents week over week. WHAT YOU'LL BRING - Customer obsession. Genuine energy being in front of customers — understanding their business and needs, thriving in complex organizations, and building trusted relationships. - Communication. Clear, direct, and persuasive across technical and non-technical audiences. - Strategic problem-solving in ambiguity. Strong analytical and critical-thinking skills to break down complex problems, spot risks early, and adapt as things change fast. - Technical aptitude. Comfortable engaging directly with technical concepts - reading API documentation, understanding data models and system architecture, and following engineering tradeoffs in a design discussion. - Ownership and delivery. A track record of running multiple complex, high-visibility projects at once and driving them to outcomes. EVEN BETTER... - Experience building or deploying AI/LLM agents in production — hands-on with the tools behind them (eval frameworks, agent tooling, RAG, prompt engineering) — working closely with technical counterparts. - You've been a founder, founding team member, or operator — you know how to balance craft, ownership, and speed. - A degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience — and/or an MBA or comparable experience at the intersection of technology and business. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits through Carrot - Lunch, as well as delicious snacks and coffee to keep you energized - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply even if your experience doesn't precisely match the job description. We strive to evaluate all applicants consistently without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.
Strategist, Agent Development (Spanish speaking)
ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/. Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. WHAT YOU'LL DO As an Agent Strategist https://sierra.ai/blog/agent-strategist-your-phd-in-applied-ai, you own how Sierra's AI agents come to life for our customers — from first pilot to a production agent handling thousands of conversations a day. You sit at the intersection of product, building agents and customer-facing work, and stay just as close to the technology as you are to the people you build for. You transform a customer's thorniest, highest-stakes problems into agents that measurably move their business. - Own the Agent Development Life Cycle https://sierra.ai/blog/agent-development-life-cycle. Take agents from initial scope through conversation design, tooling, and evaluation, to launch and continuous iteration in production — translating business goals into an agent that outperforms, and tuning relentlessly until it's the best version of itself in the real world. You'll have real ownership and autonomy over the outcome. - Be the trusted strategic advisor to enterprise leaders. Sit shoulder-to-shoulder with our customers' executives, understand what success actually means to their business, and shape the AI strategy that gets them there. - Partner with the customer's working teams day to day. Run working sessions, troubleshoot the agent as issues come up, and make the calls that keep the build on track for launch. - Directly impact product. Surface unmet needs from your customer work, and feed them back to research and platform to shape where Sierra goes next. EXAMPLE PROJECTS These are the kinds of projects that strategists on our team have worked on recently: - Scope and launch a subscription-retention agent for a large telecommunications or media company that consistently outperforms human agents at saving at-risk customers. - Stand up an agent for a commerce customer that troubleshoots a broken device end-to-end and personalizes product recommendations in the same conversation. - Redesign the conversation flows for a financial services agent so it can handle complex, regulated interactions — and convert a pilot into a multi-team production deployment. See our financial services blog https://sierra.ai/blog/five-ways-to-use-ai-agents-financial-services for examples. - Partner with product on a net-new capability — a new agent architecture or self-service tooling — using a live customer as the design partner. - Define the evaluation and quality bar for a new agent, then use those metrics to drive a measurable lift in outcome https://sierra.ai/blog/outcome-based-pricing-for-ai-agents week over week. WHAT YOU'LL BRING - Customer obsession. Genuine energy being in front of customers — understanding their business and needs, thriving in complex organizations, and building trusted relationships. - Communication. Clear, direct, and persuasive across technical and non-technical audiences. - Strategic problem-solving in ambiguity. Strong analytical and critical-thinking skills to break down complex problems, spot risks early, and adapt as things change fast. - Technical aptitude. Comfortable engaging directly with technical concepts - reading API documentation, understanding data models and system architecture, and following engineering tradeoffs in a design discussion. - Ownership and delivery. A track record of running multiple complex, high-visibility projects at once and driving them to outcomes. EVEN BETTER... - Experience building or deploying AI/LLM agents in production — hands-on with the tools behind them (eval frameworks, agent tooling, RAG, prompt engineering) — working closely with technical counterparts. - You've been a founder, founding team member, or operator — you know how to balance craft, ownership, and speed. - A degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience — and/or an MBA or comparable experience at the intersection of technology and business. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits through Carrot - Lunch, as well as delicious snacks and coffee to keep you energized - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply even if your experience doesn't precisely match the job description. We strive to evaluate all applicants consistently without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.
Forward Deployed Engineer - Software Engineer - Un...
About ElevenLabs ElevenLabs is an AI research and product company transforming how we interact with technology. We launched in January 2023 with the first human-like AI voice model. Today, we serve millions of users and thousands of businesses - from fast-growing startups to large enterprises like Deutsche Telekom and Meta. Our investors are some of the world's most prominent, including Andreessen Horowitz, ICONIQ Growth and Sequoia. We've raised $781M in funding and our last valuation was $22B - multiples of 11, always. We have expanded from voice into three main platforms: - ElevenAgents enables businesses to deliver seamless and intelligent customer experiences, with the integrations, testing, monitoring, and reliability necessary to deploy voice and chat agents at scale. - ElevenCreative empowers creators and marketers to generate and edit speech, music, image, and video across 70+ languages. - ElevenAPI gives developers access to our leading AI audio foundational models. Everything we do is the result of the creativity and commitment of our team - builders doing the best work of their lives. We are researchers, engineers, and operators. IOI medalists and ex-founders. If you want to work hard and create lasting positive impact, we want to hear from you. How we work - High-velocity: Rapid experimentation, lean autonomous teams, and minimal bureaucracy. - Impact not job titles: We don’t have job titles. Instead, it’s about the impact you have. No task is above or beneath you. - AI first: We use AI to move faster with higher-quality results. We do this across the whole company—from engineering to growth to operations. - Excellence everywhere: Everything we do should match the quality of our AI models. - Global team: We prioritize your talent, not your location. What we offer - Innovative culture: You’ll be part of a generational opportunity to define the trajectory of AI, surrounded by a team pushing the boundaries of what’s possible. - Growth paths: Joining ElevenLabs means joining a dynamic team with countless opportunities to drive impact - beyond your immediate role and responsibilities. - Learning & development : ElevenLabs proactively supports professional development through an annual discretionary stipend. - Social travel : We also provide an annual discretionary stipend to meet up with colleagues each year, however you choose. - Annual company offsite: Each year, we bring the entire team together in a new location - past offsites have included Croatia and Italy. - Co-working : If you’re not located near one of our main hubs, we offer a monthly co-working stipend. About the role You’ll use your creativity, problem-solving, and technical expertise to design tailored solutions that make a real impact. Embedded with our most strategic customers, you’ll get a front-row seat to some of our highest-stakes projects. From brainstorming technical architectures and product features to building full-scale integrations, you’ll take full ownership of complex, high-impact challenges—helping clients achieve their goals and push the limits of what’s possible. What you will do: - Take full ownership of end-to-end execution of major projects for our most strategic partners, working hands-on to deliver high-impact solutions. - Collaborate daily with our customer’s engineers and executives teams to ensure the best use of ElevenLabs’ technologies. - Drive real innovation by using your coding and technical skills to solve complex problems, making a real difference for our customers. Requirements - Experience working with customers. It’s ok if you only worked with customers in student clubs or side projects, as long as you are interested in working closely with them on a technical capacity - Proficiency in Python and strong software development knowledge, inclusive of a deep understanding of software development, software architecture, and APIs integration. - Excellent communication and problem-solving skills. Especially in terms of ability to summarize complex technical knowledge and using logic in pursuing optimal solutions. Location This role is remote-first, so it can be executed from anywhere in the United Kingdom. There is a preference for candidates to be based in London, with the option to work out of our office. Expect regular travel for customer onsites. LI-remote We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or other legally protected statuses.
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