[C] Research Engineer, Discovery
Anthropic · San Francisco, CA | New York City, NY
About this role
## About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems—safe and beneficial for users and society. ## About the Team The team is organized around the north star goal of building an **AI scientist**: a system capable of solving long-term reasoning challenges and core capabilities to push the scientific frontier. ## About the Role (Research Engineer, Discovery) As a **Research Engineer**, you’ll work end-to-end across the model stack, identifying and addressing key infrastructure blockers on the path to scientific AGI. You should be familiar with language model training, evaluation, and inference, and be eager to ramp up quickly in areas outside your current expertise. This may include: - Performance optimization - Distributed systems - VM/sandboxing/container deployment - Large-scale data pipelines ## Responsibilities - Design and implement large-scale infrastructure to support AI scientist training, evaluation, and deployment across distributed environments - Identify and resolve infrastructure bottlenecks blocking scientific progress - Develop robust evaluation frameworks to measure progress toward scientific AGI - Build scalable, performant VM/sandboxing/container architectures for safe execution of long-horizon AI tasks and scientific workflows - Collaborate to translate experimental requirements into production-ready infrastructure - Develop large-scale data pipelines for advanced language model training - Optimize large-scale training and inference pipelines for stable, efficient reinforcement learning ## You may be a good fit if you: - Have **6+ years** of highly relevant infrastructure engineering experience with expertise in large-scale distributed systems - Communicate clearly and enjoy collaborative work - Have deep knowledge of performance optimization and system architectures for high-throughput ML workloads - Have experience with containerization and orchestration at scale (e.g., **Docker, Kubernetes**) - Have a proven track record building large-scale data pipelines and distributed storage systems - Can diagnose and resolve complex infrastructure challenges in production - Can work across the full ML stack (data pipelines → performance optimization) - Have experience collaborating with researchers to scale experimental ideas - Thrive in fast-paced environments and iterate from experimentation to production ## Strong candidates may also have: - Experience with language model training infrastructure and distributed ML frameworks (e.g., **PyTorch, JAX**) - Background 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 (e.g., **AWS, GCP**) at enterprise scale - Familiarity with VM and container orchestration - Experience with workflow orchestration tools and experiment management systems - Experience working with large-scale reinforcement learning - Comfort with large-scale data pipelines (e.g., **Beam, Spark, Dask**) ## Compensation **Annual Salary:** **$350,000 — $850,000 USD** ## Logistics - **Minimum education:** Bachelor’s degree (or equivalent combination of education/training/experience) - **Required field of study:** Field relevant to the role (via coursework, training, or professional experience) - **Minimum years of experience:** Correlates with internal job level requirements - **Location-based hybrid policy:** Expect to be
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