Product Manager - Deployment

Job summary

Responsibilities

As Product Manager for Deployment, you will own how Thinking Machines' models and fine-tuned checkpoints go from training into production use. You will shape the path from a trained model to a served, reliable, cost-effective endpoint — covering inference infrastructure, serving APIs, latency and…

• Own deployment strategy, roadmap, and success metrics for taking models and Tinker-trained checkpoints into production, in close partnership with infrastructure, research…
• Define priority deployment paths and workflows across model serving, autoscaling, versioning, rollback, monitoring, and incident response
• Work at engineering depth on serving architecture, latency and cost tradeoffs, reliability targets, capacity planning, and API/SDK surfaces for deployment
• Build direct feedback loops with users deploying models in production, and turn scattered signals into a clear view of what's broken, what's missing, and what to prioritize next
• Drive ambiguous workstreams end to end: technical scoping, dependency resolution, launch readiness, on-call/escalation design, and post-incident learning

What they're looking for

• Experience owning a production ML serving, infrastructure, or deployment product, with direct involvement in reliability, scaling, or performance decisions
• Track record working at engineering depth with production systems — comfortable discussing latency, throughput, autoscaling, rollback, or incident response in specifics
• Experience taking a technical product from early usage through to reliable, scaled production use
• Background as an engineer or technical founder before moving into product leadership
• Experience with ML inference infrastructure specifically (model serving frameworks, GPU scheduling, batching, quantization tradeoffs, or similar)