Teams at Thinking Machines Lab

Own full-cycle recruiting for go-to-market roles, including account executives, sales leaders, solutions engineers, partnerships, customer success, and revenue operations. Partner with GTM leadership on role definition, sourcing, assessment, interview design, candidate closing, and hiring processes. Build talent pipelines, evaluate sales capabilities, deliver strong candidate experiences, track recruiting metrics, and improve recruiting tools, workflows, compensation, and leveling guidance.

Build and operate distributed production inference systems for large-scale AI models. Responsibilities include request routing, load balancing, batching, multi-node serving, GPU utilization, latency and throughput optimization, observability, inference engine development, debugging, and production on-call support. Collaborate with research and infrastructure teams to deploy evolving model architectures reliably and efficiently.

Design and optimize infrastructure for large-scale reinforcement learning and post-training workloads. Improve distributed training reliability, scalability, throughput, observability, and reproducibility. Build evaluation and benchmarking systems for model helpfulness, safety, and factuality, while collaborating with researchers to productionize algorithms and sharing technical learnings through documentation, open-source work, or reports.