Systems Research Engineer Jobs

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

Perplexity is seeking an experienced Machine Learning Research Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking. Responsibilities Relentlessly push search quality forward — through models, data, tools, or any other leverage available Architect and build core components of the search platform and model stack Design, train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models Conduct advanced research in representation learning, including contrastive learning, multilingual, and multimodal modeling for search and retrieval Deploy models — from boosting algorithms to LLMs — in a scalable and performant way Build and optimize RAG pipelines for grounding and answer generation Collaborate with Data, AI, Infrastructure, and Product teams to ensure fast and high-quality delivery Qualifications Deep understanding of search and retrieval systems, including quality evaluation principles and metrics Proven track record with large-scale search or recommender systems Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models Expertise in representation learning, including contrastive learning and embedding space alignment for multilingual and multimodal applications Strong publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, CVPR, SIGIR) Self-driven, with a strong sense of ownership and execution Minimum of 3 years (preferably 5+) working on search, recommender systems, or closely related research areas

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Applied AI Research Engineer Applied AI Research Team, Applied AI About the role: As an Applied AI Research Engineer, you will discover what Anthropic's models and products can do, prove it with real customers, and equip the rest of Applied AI to sell and deliver it. You'll embed with product teams to build deep expertise on new capabilities as they develop, and serve as a critical feedback loop carrying field reality back to Product and Research so that what we build next reflects what customers actually need. In collaboration with Sales, Product, and Research teams, you'll sit at the intersection of what we build and how customers adopt it. You'll own the technical GTM handshake for model and product launches: testing new models to understand their strengths and limitations, building demos and evals, and helping field teams get ready. When strategic customers hit novel challenges, you'll go hands-on to solve them. And throughout, you'll synthesize what you're learning across customers and products into insights that shape our roadmap, working closely with Product, Engineering, and Research teams to stay calibrated on what's working, what's not, and what matters most. This role is equal parts researcher, field expert, and connective tissue between our customers and the teams that build our products. You'll need the technical depth to push the boundaries of what our models can do, strong communication skills to make complex capabilities clear to diverse audiences, and the instinct to turn what you learn in the field into signal that makes our products better. Responsibilities: Embed with product teams to build dee

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: The integration team is responsible for developing and scaling machine learning algorithms and infrastructure for LLM post-training, with a focus on large-scale, distributed RL methods. We strive for excellence in both engineering and science by meticulously designing experiments and design docs. While tasks are assigned according to everyone’s expertise, there is a global team effort to write production code and support the team research efforts, depending on individual interests and organizational needs. In particular, this role aims to enhance the global quality of the post-training codebase by implementing new tools to ease and support research, optimizing post-training algorithms, and scaling distributed RL to unprecedented levels. Please Note: We have offices in London, Paris, Toronto, San Francisco, New York but we are also remote-friendly! Applicants for this role may work anywhere between UTC−06:00 and UTC+01:00. Key Responsibilities: Design and write high-performing and scalable software for training models. Develop new tools to support and accelerate research and LLM training. Coordinate with other engi

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic Fellows Program overview The Anthropic Fellows Program is designed to foster AI research and engineering talent. We provide funding and mentorship to promising technical talent - regardless of previous experience. Fellows will primarily use external infrastructure (e.g. open-source models, public APIs) to work on an empirical project aligned with our research priorities, with the goal of producing a public output (e.g. a paper submission). In one of our earlier cohorts, over 80% of fellows produced papers. We run multiple cohorts of Fellows each year and review applications on a rolling basis. Apply at the bottom of this page. We are accepting applications on a rolling basis for the next cohort expected to start in January 2027. In some circumstances, we can accommodate fellows starting outside the usual cohort timelines — please note in your application if the January 2027 start date doesn't work for you. What to expect 4 months of full-time research Direct mentorship from Anthropic researchers Access to a shared workspace Connection to the broader AI safety and security research community Weekly stipend of 3,850 USD / 2,310 GBP / 4,300 CAD + benefits (these vary by country) Funding for compute (~$15k/month) and other research expenses Interview process The interview process will include an initial application, assessments, and interviews. We may ask for references later in the process as well. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being fr

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Every time Claude runs code, uses a computer, or works as an agent, it does so inside a sandbox. Our Sandboxing team builds the isolation layer behind that work. The same systems run the code Claude writes for users, the environments we train and evaluate models in, and the agents our customers deploy. These workloads are untrusted by design, and there are a lot of them. Sandboxes need to start in milliseconds, hold up under adversarial code, and scale to a very large number of concurrent sessions. You will work at the boundary between the Linux kernel, virtualization, and the container ecosystem. You will partner with research, product, and security teams to make sure that safe and fast do not trade off against each other. Key responsibilities Design, build, and operate the sandboxing infrastructure that runs untrusted, model-generated code and agent workloads at scale Improve sandbox startup time, density, and throughput, including snapshot and restore, checkpointing, and warm pools Strengthen isolation across compute, filesystem, and network, including egress controls and per-session credential handling Investigate and fix performance, reliability, and security issues at the kernel, hypervisor, and container runtime level Build the monitoring, tracing, and debugging tools that show where time and resources go inside a sandbox Work with researchers to support reinforcement learning environments and evaluations that need fast, reproducible, isolated execution Work with product and platform engineers to expose sandboxes as a dependable, well-designed capability for Claude products and customers Partner with security tea

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Frontier research runs on more than research alone. Behind every model launch, review, and cross-team program is a web of decisions, priorities, and follow-through that keeps hundreds of researchers moving in the same direction. Research Operations owns that work: we bring clarity to ambiguity, provide stability during change, and continually deepen our domain knowledge so that researchers can spend their time on the problems only they can solve. It is one of the highest-leverage functions at Anthropic. We are hiring an experienced operator to embed with the leadership of our reinforcement learning (RL) organization, the team responsible for training Claude to be more capable, more reliable, and more aligned. You will shape what leadership spends its time on, make sure priority decisions reach the right decision-makers and get made quickly, and clear the path so leaders can focus on the work that matters most. You will report to the Manager of Research Operations and work daily with RL org leaders and their teams. Key responsibilities Own leadership's time. Shape the week around the leaders' specific, most timely priorities, set agendas so meetings are spent on decisions that move the org forward, and cut what isn't critical so leaders stay focused on the areas that are highest-leverage for them. Triage what reaches leadership. Resolve what you can, route what belongs elsewhere, and thoughtfully escalate the rest. Enable wise decision-making and follow through. Give leaders what they need to decide quickly, communicate those decisions to collaborators, and track commitments through to completion. See problems coming. Su

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role: The Encodings Infra team maintains the libraries that engineers and researchers across Anthropic use to encode text and multimodal data into a form that Claude can consume. It also determines Claude’s prompt shape: how a user’s turn is represented to the model, how Claude calls tools and receives tool results, and so on. As a Software Engineer on this team, you'll own the design and maintenance of these libraries—keeping their APIs intuitive, their performance sharp, and their abstractions solid enough that most of the org never has to think about encodings or prompt structures at all. You’ll have the satisfaction of knowing that your work enabled Claude to learn new ways of understanding the world. This role is unusually broad: your work will touch systems across the codebase, from pretraining to finetuning to the API, and you'll collaborate closely with both researchers and engineers to make sure new encoding ideas can move quickly from experiment to production. Responsibilities: Maintain and improve the encoding libraries used by engineers and researchers across Anthropic Run experiments to determine the optimal way to feed structured data into Claude without confusing it Design data structures and abstractions that shield most of the organization from the details of how encoded data works while enabling “power users” Adapt the encoding libraries to support new research directions as they emerge, and make sure that we can ship these research ideas to production Optimize encoding performance across the systems that depend on these libraries You may be a good fit if you: Have 5+ years of software engineering experienc

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role We're building Anthropic's search platform team to create scalable, efficient search capabilities that power Claude's ability to access and retrieve information. In this role, you'll design and implement core search systems from the ground up. Responsibilities: Work with a small, senior team of engineers and researchers to build Anthropic’s search platform from scratch Establish metrics and tooling to measure and improve search quality Enhance search algorithms and indexing mechanisms to boost search accuracy and efficiency You may be a good fit if you: 5+ years building complex distributed systems in production Solid grasp of search fundamentals: indexing, retrieval, ranking Strong coding skills in Python, Go, Java, or similar languages Hands-on experience with high-volume data processing architectures The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, s

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Performance Engineer, Inference Engine About the Role Anthropic's inference engine is the software between the accelerator kernels and the routing layer. It manages the entire token path in between: batching requests, laying the model out across chips, managing memory for weights and activations, coordinating every forward pass, and managing model state across requests. Built in-house, it runs on all of our accelerator platforms, serving Claude to millions of users and running our research workloads. You will work on building and optimizing this system at Anthropic scale: improving throughput, cost, reliability, and latency across all accelerator and cloud platforms. You are intimately familiar with the hardware and bandwidth numbers (FLOPs, HBM, PCIe, RDMA, network links, etc.) and can model a problem quickly: where the time and bytes go, and what sets the bound. The role is deeply technical and high-impact, and suits engineers who enjoy working across accelerator programming, high-performance systems that seamlessly coordinate between host and device, and large-scale distributed systems. Familiarity with the transformer architecture is a plus. Some example recurring themes: Keep device utilization high. Accelerators should never be waiting due to other overheads. Reuse instead of recompute. Keep model state cached and reuse it whenever that is cheaper than computing it again. Measure, model, then change. We build the observability to see where the gaps are, model the impact of potential improvements, deploy them, and go around again, with Claude speeding up every turn of that loop. Tokens you can trust. Ensuring model quality matter

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Life Sciences team is building a research group focused on biological discoveries by combining AI with hands-on biological research. We're seeking research scientists with deep medicinal chemistry expertise to join the team. Our goal is to speed up progress in the life sciences by an order of magnitude, and this role puts that mission into practice. You'll be an expert medicinal chemist with hands-on experience designing, synthesizing, purifying, and characterizing small molecules. Above all, you'll close the loop between AI-generated hypotheses and experimental data to share with the community. Working alongside AI researchers and engineers, you'll drive experimental programs end to end and pressure-test AI-designed molecules and AI-proposed synthetic routes against real chemistry. This role is fully on-site in the San Francisco Bay Area. Key responsibilities Design multi-step synthetic routes for small molecules and oversee their execution end to end, including route selection, reaction optimization, purification strategy, and interpretation of full characterization data (NMR, LC-MS, HPLC) Coordinate synthesis and biological evaluation with external contract research organizations and academic partners Evaluate AI-designed molecules and AI-proposed synthetic routes, drive them to experimental readout, and feed results back to research teams Apply medicinal chemistry principles, including SAR analysis and physicochemical property optimization, to compound design and prioritization Ensure experimental data across programs is high quality and well documented, including procedures, spectra, and failure modes C

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic is looking for an Engineering Manager to lead the Search Platform team. This team builds the search stack behind Claude: the indexes, retrieval and ranking systems and serving infrastructure that power web search across claude.ai , the API and agentic surfaces. Search is core to how Claude answers questions about the world, and the team owns both the problem and the solution end to end: growing the index, improving retrieval quality and running the serving stack at production scale. The same platform also serves research workloads, so demand comes from multiple user surfaces, often at the same time. The role carries a product dimension. Search sits close to the user experience and PM coverage is thin at times, so often the EM helps decide what a good search experience looks like, not just how to build it. You'll partner with product teams shipping search-backed features, research teams that depend on retrieval quality and capacity, and the infrastructure teams that run the systems underneath. We're looking for someone with real search experience who can be hands-on when needed: reviewing designs, digging into relevance regressions and holding their own in technical debates. Key Responsibilities Lead and grow the team of engineers building Anthropic's search platform: indexing, retrieval, ranking and serving Own the strategy and roadmap for the platform and how Claude's search needs are met over time Own search quality: evaluation methodology, relevance measurement, regression detection and the ranking improvements they drive Operate the platform at scale, balancing product traffic against research and training

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Code RL at Anthropic drives reinforcement learning efforts behind Claude's coding capabilities, creating and scaling agentic coding environments. This is an engineering role with unusual latitude to set technical direction and standards. You'll be part of a team solving the engineering side of research efforts such as embedding with research teams, getting up to speed on their systems and needs, and designing the frameworks, APIs, and infrastructure that let researchers move faster, then rotating off, leaving behind well-oiled systems those teams can understand, own, and maintain themselves. Your remit also includes the ongoing health of production RL runs: maintainable, monitored, and straightforward to triage. The team's problem space spans the client side of sandboxed execution for agentic RL environments, large-scale data processing jobs, the lifecycle of production datasets, and the frameworks researchers build environments on. You won't own all of this yourself, you'll take on the slices where your depth matters most. You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and hard-won intuition for how complex systems fail — especially silently. You should be comfortable diving into messy research code, finding the load-bearing abstractions, and improving them incrementally while researchers continue to build on top of your work. Key responsibilities Design widely-used APIs, frameworks, and abstractions that other engineers and researchers build on, with careful attention to interface legibility and principled defaults Embed with research teams on a rota

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's Environments organization builds and maintains the infrastructure that improves Claude’s capabilities through reinforcement learning. That includes the frameworks researchers use to build environments and the infrastructure responsible for running them. The team's mission is to productionize research. You'll embed with research teams, get up to speed on how they work, and design the frameworks and APIs that let them move faster, building systems the team can understand, own, and maintain themselves. Scope also includes keeping production RL runs healthy, maintainable, monitored, and easy to triage. You'll be a strong fit if you have deep expertise in Python, a refined sense of taste for API and framework design, and good intuition for how complex systems fail, especially silently. It's a bonus if you've built and operated a stateful distributed system, such as a workflow engine, actor framework, or durable-execution runtime, where correctness depends on getting shared state and recovery right. You should be comfortable diving into messy research code, finding the abstractions that matter, and improving them incrementally while researchers continue to build on your work. You should also be comfortable using AI tools to accelerate your own development, but have an impulse towards deep verification. Key responsibilities Design widely used APIs, frameworks, and abstractions that other engineers and researchers build on, making correct usage the default and ruling out entire classes of errors structurally Own the platform layers that sit beneath every environment, including the agent runtime Build the

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the team Anthropic's Life Sciences team is building a world-class research group focused on making fundamental biological discoveries. The team combines cutting-edge AI with hands-on biological research, positioning Anthropic at the forefront of AI-accelerated scientific discovery. About the role We're seeking an exceptional Research Scientist to join the team. This role combines deep computational biology expertise with frontier AI capabilities, positioning Anthropic at the forefront of AI-driven scientific discovery. As one of the first computational members of this Life Sciences research group, you'll work on a high-impact team that operates at the intersection of computational and experimental biology. You'll bring broad computational biology experience to bear across the team's projects, driving discoveries from large-scale computational analysis of biological data through to results our experimental scientists can test, and moving flexibly between problems as the science demands. You'll have substantial access to Claude and you'll help establish how computational biology operates at Anthropic. This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for scientific understanding and biomedicine. If you're excited about using your computational expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you. Key responsibilities Build, run, and maintain the analysis pipelines that back the team's experimental programs: sequence a