Thinking Machines LabSan Francisco, CABuild and standardize AI-powered coding tools, agents, and dev environments to accelerate internal software development while maintaining security and quality. Requires experience with productivity tooling for large codebases, container/CI platforms, and AI model APIs.
Minimum qualifications:
Preferred qualifications:
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.