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.
Research Engineer, Discovery at Anthropic
Hybrid - San Francisco, CA, United States
More jobs at AnthropicSalary
USD 350,000 - 850,000
Requirements
Skills
- 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
- Strong communication skills and collaborative mindset
- 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
- Excellent at diagnosing and resolving complex infrastructure challenges in production environments
- Ability to work effectively across the full ML stack from data pipelines to performance optimization
- Experience collaborating with researchers to scale experimental ideas
- Thrives in fast-paced environments and can rapidly iterate from experimentation to production
- 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, …)
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 toward 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
Technologies
DockerKubernetesVM/sandboxing/container deploymentDistributed systemsLarge scale data pipelinesDistributed storage systemsAWSGCPBeamSparkDaskPyTorchJAXGPU/TPU architecturesWorkflow orchestration toolsExperiment management systems
See if your resume is ready for this job
See how our AI can optimize your resume and improve your chances for this role.