Research Engineer on the Discovery team will work end-to-end across the model stack, addressing infra blockers for scientific AGI. The role involves designing large-scale infrastructure, optimizing training and inference pipelines, building data pipelines, and collaborating with researchers to translate experimental needs into production‑ready systems.
Research Engineer, Discovery en Anthropic
Híbrido - San Francisco, CA
Más vacantes en AnthropicSalary
USD 350,000 - 850,000
Requirements
Skills
- 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
- 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 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
Technologies
DockerKubernetesAWSGCPPyTorchJAXBeamSparkDask
Descubre si tu currículum está listo para esta vacante
Mira cómo nuestra IA puede optimizar tu currículum y aumentar tus chances en este puesto.