Research Engineer, Discovery

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+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems - Are a strong communicator and enjoy working collaboratively - Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads - Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale - Have proven track record of building large-scale data pipelines and distributed storage systems - Excel at diagnosing and resolving complex infrastructure challenges in production environments - Can work effectively across the full ML stack from data pipelines to performance optimization - Have experience collaborating with other researchers to scale experimental ideas - Thrive in fast-paced environments and can rapidly iterate from experimentation to production Strong candidates may also have: - Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.) - Background in building infrastructure for AI research labs or large-scale ML organizations - Knowledge of GPU/TPU architectures and language model inference optimization - Experience with cloud platforms (AWS, GCP) at enterprise scale - Familiarity with VM and container orchestration. - Experience with workflow orchestration tools and experiment management systems - History working with large scale reinforcement learning - Comfort with large scale data pipelines (Beam, Spark, Dask, …) 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: $350,000—$850,000 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, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. 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 from underrepresented groups are more prone to experiencing im

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: 6+ years highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems. Strong candidates may have experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.). Knowledge of GPU/TPU architectures and language model inference optimization. Experience with containerization technologies (Docker, Kubernetes) and orchestration at scale. Proven track record of building large-scale data pipelines and distributed storage systems. Experience with cloud platforms (AWS, GCP) at enterprise scale. Familiarity with VM and container orchestration. Experience with workflow orchestration tools and experiment management systems. History working with large scale reinforcement learning. Comfort with large scale data pipelines (Beam, Spark, Dask, …). Visa sponsorship: Yes (explicit). Location-based hybrid policy: Staff expected to be in offices at least 25% of the time.