over 1 year ago
Base Salary
$315k - $560k/yr
Responsibilities
- Design and implement large-scale infrastructure for AI scientist training, evaluation, and deployment across distributed environments.
- Identify and resolve infrastructure bottlenecks affecting scientific capabilities.
- Develop reliable evaluation frameworks for measuring progress toward scientific AGI.
- Build scalable VM, sandboxing, and container architectures for safely executing long-horizon AI tasks and scientific workflows.
- Translate experimental requirements into production-ready infrastructure in collaboration with researchers.
- Develop large-scale data pipelines for advanced language model training.
- Optimize distributed training and inference pipelines for stable and efficient reinforcement learning.
Requirements
- At least 3 years of highly relevant infrastructure engineering experience with expertise in large-scale distributed systems.
- Deep knowledge of performance optimization and system architectures for high-throughput ML workloads.
- Experience with Docker, Kubernetes, large-scale data pipelines, distributed storage systems, and production infrastructure troubleshooting.
- Ability to work across the full ML stack and collaborate with researchers to scale experimental ideas.
- A bachelor’s degree in a related field or equivalent experience.
- Experience with language model training infrastructure, PyTorch, JAX, GPU/TPU architectures, language model inference optimization, AWS, GCP, workflow orchestration, experiment management, reinforcement learning, Beam, Spark, or Dask is advantageous.
Benefits
- Annual salary range of $315,000-$560,000 USD.
- Hybrid policy requiring staff to work from an office at least 25% of the time, with some roles requiring more.
- Visa sponsorship may be available, with immigration-lawyer support.
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Tech Stack
Categories
About Anthropic
Anthropic builds large language models and the Claude AI assistant for developers and enterprises, offered via API access and enterprise plans. Founded in 2021 and headquartered in San Francisco, it distributes Claude through its own platform and via partners such as Amazon Bedrock and Google Cloud’s Vertex AI. Its work emphasizes model reliability, interpretability, and practical tooling for tasks like coding assistance, analysis, and customer support automation.