Research Engineer, Pretraining Scaling - London

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 is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pretraining team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. Key Responsibilities: Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development Independently lead small research projects while collaborating with team members on larger initiatives Design, run, and analyze scientific experiments to advance our understanding of large language models Optimize and scale our training infrastructure to improve efficiency and reliability Develop and improve dev tooling to enhance team productivity Contribute to the entire stack, from low-level optimizations to high-level model design Qualifications: Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field Strong software engineering skills with a proven track record of building complex systems Expertise in Python and experience with deep learning frameworks (PyTorch preferred) Familiarity with large-scale machine learning, particularly in the context of language models Ability to balance research goals with practical engineering constraints Strong problem-solving skills and a results-oriented mindset Excellent communication skills a

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,

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 , 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 a

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

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 RL Scaling Science team studies how reinforcement learning behaves as we scale it (across model size, compute, and task horizon) and turns that understanding into the training recipes behind our frontier models. As a Research Engineer on this team, you'll design and run large-scale experiments to understand and resolve bottlenecks, build the benchmarks that make long-horizon progress measurable, and ship validated findings directly into production training. This role lives at the boundary between research and engineering. The problems are open, the experiments run at frontier scale, and the path from a robust result to production is short. Key responsibilities Design, run, and interpret large-scale RL experiments, reasoning rigorously about what the data does and doesn't show Investigate how RL improves as horizon, compute, and model size grow Build and maintain benchmarks for long-horizon RL so progress is measurable and reproducible Translate validated findings into production training recipes, exercising judgment about when a result is robust enough to ship Debug complex issues at the seam where research meets infrastructure - failures that only appear at scale Partner closely with adjacent RL teams across research and engineering and advance our overall RL stack Minimum qualifications Strong empirical research skills in Reinforcement Learning, large-scale ML training, or a closely adjacent area Demonstrated ability to own large experiments end-to-end, from design through interpretation Proficiency in Python and experience working with large-scale or distributed ML systems Comfort operating at the researc

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 la