Research Engineer, Discovery
Anthropic
Summary
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.
Job description
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.
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, …)
Requirements
- 6+ years of highly-relevant experience in infrastructure engineering
- Demonstrated expertise in large-scale distributed systems
- Deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
- Experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
- Proven track record of building large-scale data pipelines and distributed storage systems
- Ability to diagnose and resolve complex infrastructure challenges in production environments
- Ability to work effectively across the full ML stack from data pipelines to performance optimization
- Experience collaborating with other researchers to scale experimental ideas
- Strong communication and collaboration skills
- Bachelor's degree or equivalent combination of education, training, and/or experience
Benefits
- Equity donation matching
- Generous vacation
- Parental leave
- Flexible working hours
Company overview
Anthropic is a leading artificial intelligence research company founded in 2021 and headquartered in San Francisco, California. The company specializes in developing safe, reliable, interpretable, and steerable AI systems with a strong emphasis on AI safety and ethical development. Anthropic's flagship product is Claude, an advanced AI assistant capable of handling complex tasks at any scale, which has gained significant traction in enterprise applications. The company was founded by former OpenAI executives Dario Amodei (CEO) and Daniela Amodei (President), bringing extensive experience in AI research and safety. Anthropic's research interests span multiple areas including natural language processing, human feedback systems, scaling laws, reinforcement learning, code generation, and AI interpretability. With a valuation reaching $18.4 billion and significant funding rounds, Anthropic has established itself as a major player in the competitive AI landscape, focusing on providing trustworthy AI solutions for enterprise clients while advancing the frontier of AI safety research.