Software Engineer, RL Data

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. About the Role One of our north stars is a future where teams can hire Grok as a remote colleague. Reaching that future requires training our models in realistic, diverse environments that support complex, end-to-end work across domains. As a Software Engineer on the RL Environments team at SpaceXAI, you’ll build the systems that turn real-world data into high-quality environments and tasks for reinforcement learning. You’ll create dramatically more realistic training environments while owning the shared platforms and quality layers that help us produce trustworthy data quickly and at scale. This role sits at the intersection of research, data, and engineering. You’ll work closely with ML Platform and research teams to turn company data, Grok Bot interactions, vendor-built tasks, tutor data, acquired data, and synthetic data into training-ready environments. Your work will make high-quality RL data easier to create, validate, discover, and use across our model-training efforts. What you’ll work on Building platforms that allows us to build complex, realistic, and diverse RL environments at scale, that support end-to-end tasks across knowledge-work domains. Designing end-to-end f

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. About the role The ML Infrastructure team builds large-scale compute, storage, and software infrastructure to support Cursor’s work building the world’s best agentic coding model. We’re looking for strong engineers who are interested in building high-performance infrastructure and the software to support it. This role works closely with ML researchers and engineers to enable their work through improvements to our training framework, systems reliability/performance, and developer experience. What you’ll do Collaborate with ML researchers to improve the throughput and reliability of training Work with OEMs, cloud service providers, and others to plan and build cutting-edge GPU infrastructure Improve the density and scalability of compute environments to enable increasingly large RL workloads Create software and systems to automate building, monitoring, and running GPU clusters Build workload scheduling and data movement systems to support Cursor’s growing training footprint You may be a fit if A strong background in systems and infrastructure-focused software engineering, particularly in Python, Typescript, Rust, and Golang Experience with distributed storage and networking infras

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. Research Engineer SpaceXAI is building the future of coding. We train frontier coding agents and scale RL on real user data to make them increasingly effective. About the role We’re looking for Research Engineers to build the training, inference, and data systems behind our frontier coding models. You’ll work directly with researchers to make progress repeatable and iteration fast. What you’ll do Build our distributed training, inference, and RL infrastructure Write libraries to simplify how researchers do large-scale data jobs Architect the systems that turn Cursor user data into effective training data You may be a fit if You have a strong infrastructure/distributed systems background You are able to architect and ship end-to-end with high ownership You have strong intuitions about how language models work You’re excited to learn more about ML #LI-DNI

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. Research Scientist SpaceXAI is building the future of coding. We train frontier coding agents and scale RL on real user data to make them increasingly effective. About the role We’re looking for Research Scientists who can drive effective RL or mid-training research in a small-team setting. You’ll own ambiguous, hard research problems end-to-end: forming hypotheses, designing experiments, building the training/eval/data needed to test them, and pushing results into the next model. You should expect significantly more scope and autonomy than in other research labs. What you’ll do Improve our understanding of RL, what it takes to handle longer horizon tasks, and train with less compute Train graders to improve performance on coding tasks with non-verifiable reward Improve the quality and difficulty of datapoints we use for training our models Realtime RL for coding agents You may be a fit if You have a deep background in RL and strong machine learning fundamentals You’re an excellent programmer and software engineer You can handle ambiguous research tasks with little guidance You care a lot about data quality, and can dive into the data when appropriate You are truth seeking, aimi

About the Team Our Robotics team is focused on unlocking general-purpose robotics and pushing towards AGI-level intelligence in dynamic, real-world settings. Working across the entire model stack, we integrate cutting-edge hardware and software to explore a broad range of robotic form factors. We strive to seamlessly blend high-level AI capabilities with the constraints of physical systems to improve peoples’ lives. About the Role We are hiring a Sim Infrastructure Engineer to turn simulation systems into reliable, automated, production-quality pipelines that power model training, evaluation, and hardware-in-the-loop validation. This role owns the automation, orchestration, and tool integration that apply simulation to concrete robotics tasks: building CI/CD for SIL/HIL, presubmit checks, automatic model evaluation, metric computation and reporting, and the runtime infrastructure to run simulations at scale. You will collaborate closely with Sim Realism, Sim Environments, Research, and Ops to make simulation an integrated, reproducible, and measurable part of our ML and robotics workflows. This role is based in San Francisco, CA, and requires in-person 4 days a week. In this role, you will: Build and maintain presubmit checks, continuous integration and deployment pipelines for simulation code, environments, and tasks so simulation artifacts are testable, versioned, and reproducible. Implement end-to-end automation to run model evaluation in sim (SIL) and orchestrate HIL runs; compute realism and task metrics, generate dashboards and alerts, and ensure evaluation is repeatable and auditable. Create robust APIs and connectors so research, training, and data-collection systems can schedule, seed, and evaluate batches of simulations; support RL rollouts, imitation-data collection, and presubmit model checks. Build scheduling, batching and orchestration for running very large numbers of concurrent rollouts (target tens of thousands of rollouts / large RL workloads), sol

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview As a Member of Technical Staff in Post-Training Data Infrastructure, you will build and own the pipelines that produce the post-training data for our large language models (LLMs), from SFT and RL data to synthetic and human-annotated data. You will work closely with researchers and annotation teams so they can iterate on data as quickly as they iterate on models. Key Responsibilities Build and maintain data pipelines for SFT, preference, and RL post-training data. Own dataset quality, versioning, and reproducibility. Turn research data recipes into production-grade, reusable components. Scale model inference for data generation and model-based grading. Design data formats and schemas that keep post-training datasets consistent and efficient to train on. Collaborate with researchers and engineers to make sure data pipelines keep pace with the needs of cutting-edge language models. Qualifications Strong software engineering skills. Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training. Track record of running distributed data processing and model inference at