Reliability Engineer, Supercomputing
Ensure reliability of large GPU supercomputing clusters by diagnosing hardware/firmware/OS issues, automating monitoring, driving firmware rollouts, and working directly with vendors.
Builds and scales core infrastructure for AI model training, data systems, and developer tools in a high-impact team. Requires backend proficiency (Python/Rust), experience with large-scale clusters like Kubernetes, and end-to-end project ownership.
We interview generally, but during project selection we’ll take into account your interests and experience alongside organizational needs.
Example areas you may contribute to:
Core Infrastructure: Support teams that train, research, and serve AI models. Build systems for large Kubernetes clusters with GPU workloads, or infrastructure to support Tinker.
Data Infrastructure: Design and optimize data pipelines using tools like Spark and other modern data infrastructure technologies. Build scalable, reliable data infrastructure with governance best practices.
Developer Productivity: Build tooling, systems, frameworks for optimized developer environments.
Minimum qualifications:
Preferred qualifications:
Compensation: $350,000 - $475,000 USD annual salary, depending on background, skills and experience.
Benefits: Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, relocation support.
Ensure reliability of large GPU supercomputing clusters by diagnosing hardware/firmware/OS issues, automating monitoring, driving firmware rollouts, and working directly with vendors.
Own and debug multi-thousand-GPU network fabric (RDMA/RoCE, NVLink/NVSwitch) for large-scale AI training and inference. Requires backend language proficiency, large-scale cluster experience, and cross-stack ownership.
Performance engineer focused on cross-layer investigations of Anthropic's inference fleet for Claude, optimizing throughput, latency, reliability, and correctness while building observability and partnering with kernel and serving teams.
Site Reliability Engineer drives end-to-end reliability for AI fine-tuning platform Tinker, including SLOs, monitoring, incident response, and multi-tenant GPU scheduling. Requires distributed systems experience, software proficiency for reliability, and production incident handling.
Designs and optimizes distributed training systems scaling across thousands of GPUs for large AI models. Requires strong systems engineering, PyTorch/JAX expertise, and collaborative mindset to boost research productivity.