Systems Research Engineer Intern Jobs

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 RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-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 The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-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 teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas: Developing systems that enable models to use computers effectively Advancing code generation through reinforcement learning Pioneering fundamental RL research for large language models Building scalable RL infrastructure and training methodologies Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities. About the General Agents Team The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers. About the Role As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments. You will: Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases. Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints. Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings. Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs. Productionize frontier agent techniques (e.g.,

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 looking for a research engineer who believes that visual and spatial reasoning are core to fully unlocking the capabilities of LLMs. On the Vision team, you'll own the end-to-end process of creating training data and RL environments targeting visual knowledge work: identifying long-horizon and vision-heavy tasks, building evals, designing rewards, and scaling data. This is a unique role that combines applied research with hands-on data work. It's also highly collaborative — you'll partner with external vendors, pretraining, RL, and product teams to make sure the environments you build translate into real-world knowledge work capabilities. What you'll do: Own the data strategy for vision capabilities end-to-end, from building evals and scaling RL environments Manage technical relationships with external data vendors, including writing task specifications, evaluating visual data and annotation quality, and iterating on reward design Develop and improve QA frameworks that catch reward hacking and ensure environment quality at scale Run generalization experiments to measure how data strategy changes improve multimodal capabilities on held-out evaluations Partner with pretraining, RL, and product teams, and do the science that shows we’re all rowing in the same direction You may be a good fit if you: Have 7+ years of ML, computer vision, and software engineering experience through industry, academia, or other projects Have experience with reinforcement learning, reward design, or training data curation for large language or vision-language models Are familiar with the architecture, training, and operation of large visi

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 Reinforcement learning (RL) is how Claude learns to reason, write code, and act autonomously over long horizons. This role sits on the team that builds and owns the RL training system: the system that trains our production models and that researchers across Anthropic run their experiments on. The team works closely with research teams across the company on the science and engineering of making RL work at scale. As a Research Engineer on the team, you'll work at the center of RL at Anthropic. You'll have a direct view of how RL training behaves at the frontier because the system you own sits underneath both production and research runs. You will use it with collaborators across research teams to understand what is working, what is fragile, and where the next improvements are. Key responsibilities Build, own, and improve the core RL training system that serves Anthropic's production and research runs Work across the stack (orchestration, environments, training, inference, evaluation) wherever the system needs it Study how RL training behaves at scale and contribute to the research that improves it, in collaboration with teams across Anthropic Implement new training methods as stable, fast, well-tested code Improve the speed and efficiency of RL training and evaluation through profiling, optimization, and benchmarking Make the system easier for researchers to build on, through clean abstractions, clear APIs, and automated testing Debug hard problems across the stack, from a run that has quietly drifted to a distributed systems failure that only shows up at scale Communicate results clearly, in writing and in discussion Min

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 ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems. This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow. Responsibilities: Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams Build and maintain production logging, monitoring dashboards, and evaluation infrastructure Add new capabilities to the training codebase, such as long context support or novel architectures Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams Contribute to

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 ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems. This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow. Responsibilities: Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams Build and maintain production logging, monitoring dashboards, and evaluation infrastructure Add new capabilities to the training codebase, such as long context support or novel architectures Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams Contribute to

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 Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. About the role As a Research Engineer on our team you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines. Join us in our mission to develop advanced AI systems pushing the frontiers of science and benefiting humanity. Responsibilities: Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI. Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows Collaborate to translate experimental requirements into production-ready infrastructure Develop large scale data pipelines to handle advanced language model training requirements Optimize large scale training and inference pipelines for stable and efficient reinforcement learning You may be a good fit if you: Have 6+

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 teams Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.5 and Opus 4.5. Our work spans several key areas: Developing systems that enable models to use computers effectively Advancing code generation through reinforcement learning Pioneering fundamental RL research for large language models Building scalable RL infrastructure and training methodologies Enhancing model reasoning capabilities We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish. About the Role As a Research Engineer within Reinforcement Learning, you will collaborate with a diverse group of researchers and engineers to advance the capabilities and safety of large language models. This role blends research and engineering responsibilities, requiring you to both implement novel approaches and contribute to the research direction. You'll work on fundamental research in reinforcement learning, creating 'agentic' models via tool use for open-ended tasks such as computer use and autonomous software

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As an Threat Research Engineer at GitLab, you'll research emerging security threats and turn your findings into protection for GitLab customers. You'll investigate new attack surfaces, including AI coding agents, agent plugins, and package ecosystems, and build systems that help detect threats and improve vulnerability advisories. This is a full-stack role: you'll take an idea from the first research question to a working proof of concept, deploy it, and analyze the results yourself. Artificial intelligence (AI) is central to how we work. You'll use AI tools daily and build agentic workflows for research,

About the Team OpenAI develops models that can reason through complex problems and hardware designed for the demands of advanced AI. AI for Chips connects these efforts: applying increasingly capable AI systems to the work of semiconductor engineering. Our goal is to help engineers develop better chips and shorten design cycles. This work brings research, model training, and hardware expertise together to build tools that engineers can use on real designs, with correctness and measurable performance at the center. About the Role We’re hiring a Research Engineer to help OpenAI models solve chip-design problems through reinforcement learning, tool use, and evaluation. You’ll own experiments from the initial idea through implementation and analysis. That means building environments and evaluations, running training, investigating failures, and using the results to decide what to try next. You’ll also build the software needed to make those experiments reliable and reproducible. We value strong coding fundamentals, careful experimental judgment, and the ability to make progress independently. Prior chip-design experience is helpful, but you can learn the domain alongside the team’s hardware specialists. In this role, you will: Build RL environments and evaluations for tasks such as RTL generation, design verification, and physical design optimization. Develop and test approaches that help models use chip-design tools and improve power, performance, and area while preserving correctness. Design experiments, establish baselines, and measure whether improvements hold up on new tasks and designs. Investigate failures across model behavior, rewards, evaluation tools, and experiment infrastructure. Improve iteration speed through better tooling, faster evaluations, and proxy rewards that reflect the outcomes we care about. Turn successful experiments into reusable research code and training workflows, working closely with researchers and engineers. You might thrive in this ro

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 The Security team protects Anthropic's AI systems and maintains the trust of our users and society. About the Role At Anthropic, we are pioneering new frontiers in AI that have the potential to greatly benefit society. However, developing advanced AI also comes with risks if not properly safeguarded. As a Research Engineer, you'll help to safely advance the capabilities of our models in incident response, security analysis, vulnerability remediation, and other areas of defensive cybersecurity. This role blends research and engineering, requiring you to both develop novel approaches and realize them in code. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers, engineers, and cybersecurity specialists across and outside Anthropic. This role requires ML experience combined with domain expertise in cybersecurity. You might be an ML researcher with a history of cybersecurity work, or a security professional with a background in ML. Responsibilities Partner with researchers and safety teams across Anthropic to understand their analytical needs and build solutions Develop agentic integrations that allow AI systems to autonomously investigate and act on analytical findings Contribute to the strategic direction of the team, including decisions about what to build, what to partner on, and where to invest This role may require participation in an on-call rotation. You may be a good fit if you Have experience with machine learning. Have experience in cybersecurity research. Have strong

HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world’s most innovative companies to build strong engineering teams ready for what’s next. Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills. People at HackerRank care deeply about the impact of their work and sweat the small details so our customers can be wildly successful with products they genuinely love to use. We move with urgency and believe great outcomes come from high standards. About the role Hiring is one of the most consequential decisions a company makes. 3,000+ enterprises rely on HackerRank to get it right. We are now reinventing how that works for the agentic era. The AI/ML systems that power this platform are not auxiliary features. They are the product. Open Problems The agentic era is reshaping every layer of the hiring stack. These are some of the core problems you'll be working across, none of them fully solved. Chakra: Building an autonomous AI interviewer that conducts, adapts to, and evaluates technical interviews end to end. Integrity: Detecting fraud and suspicious behavior across multiple signal types. The ways candidates game assessments change frequently, and the models need to keep up. Evaluation: Measuring technical skill in a world where AI writes the code. The old proxies no longer hold and the new ones have not been defined yet. Your focus will shift across these depending on where the highest-leverage work is at any given time. What you will do Research, Design, and Develop advanced Deep Learning models and novel Neural architectures. Explore architectures beyond Transformers, including State Space Models (SSMs), Liquid Neural Networks (LNNs), and Hybrid approache

About the Role The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. As a Research Engineer on the Scaling Team within Model Shaping, you will turn cutting-edge research on efficient foundation model training into robust, high-performance systems. You will profile and optimize Together's training infrastructure, identify performance bottlenecks across the stack, and implement state-of-the-art techniques from both the research literature and our own scientists in production environments. Your work will directly shape the fine-tuning experience of Together's customers. You will rapidly bring newly released open-source models onto the Model Shaping platform, ensuring they train efficiently and reliably across diverse customer workloads. Working closely with Research Scientists, you will also build the experimental infrastructure that accelerates research and enables validated ideas to be deployed reliably at scale. Responsibilities Design, implement, and optimize core components of Together's large-scale training infrastructure. Integrate new model architectures, validate training correctness and convergence, and optimize performance for production fine-tuning workloads. Profile distributed training workloads to identify and eliminate bottlenecks across compute, memory, and communication. Design and execute experiments to validate performance hypotheses and benchmark new approaches against state-of-the-art methods. Partner closely with Research Scientists to productionize novel training methods and contribute to publications and o