Thinking Machines LabSan Francisco, CASite 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.
Minimum qualifications:
Preferred qualifications:
Expected annual salary range: $350,000 – $475,000 USD.
Generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
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.
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.
Designs and optimizes infrastructure for scalable reinforcement learning training of large models, improving reliability, observability, and throughput. Collaborates with researchers to productionize RL algorithms; requires strong engineering skills and deep learning framework knowledge.