Job description

ABOUT THINKING MACHINES The mission of Thinking Machines is to build AI that extends human will and judgment. We are training frontier models with Inkling, developing Tinker to let people make models their own, and crafting interfaces that broaden human-AI communication. We believe the future worth building is human, and we're hiring people who want to build it. ABOUT THE ROLE 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 throughput tradeoffs, scaling behavior, observability, and the workflows researchers and external users rely on to deploy their work with confidence. This is not a mature MLOps role at an established platform. Deployment at Thinking Machines is still being defined: what "production-ready" means for a fine-tuned model, which serving paths we support, how much control users get over performance and cost tradeoffs, and how we scale reliably as usage grows. You will work from infrastructure capability through to a deployment experience that is fast, predictable, and trustworthy. The strongest candidate has shipped and operated production ML or infrastructure systems before, ideally as an engineer before becoming a product leader, and can reason from strategy down to autoscaling behavior, latency budgets, rollout safety, and the on-call realities of running models in production. What You'll Do - Own deployment strategy, roadmap, and success metrics for taking models and Tinker-trained checkpoints into production, in close partnership with infrastructure, research, engineering, and GTM - 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 - Connect deployment decisions to the model and infrastructure roadmap, making visible the tradeoffs between flexibility, reliability, and operational cost - Shape SLAs, pricing/packaging inputs for hosted inference, and the operating model for a deployment platform expected to scale quickly - Do whatever work makes deployment succeed — reviewing a serving config, joining an incident retro, inspecting latency data, or writing the rollout plan for a new model Skills and Qualifications - 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 Preferred qualifications: - 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) - Experience operating in a startup, lab, or new product area where the deployment model and roadmap weren't handed to you - Comfortable moving between a strategic narrative and a specific technical detail (an autoscaling policy, an SLA definition, a rollout gate) without losing judgment - Experience building trust with technical users through evidence, responsiveness, and follow-through rather than process ownership Logistics - Location: This role is based in San Francisco, CA. - Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $400,000 USD. - Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together. - Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Requirements

Must-haves

Own deployment strategy, roadmap, and success metrics for taking models and Tinker-trained checkpoints into production, in close partnership with infrastructure, research, engineering, and GTM
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
Connect deployment decisions to the model and infrastructure roadmap, making visible the tradeoffs between flexibility, reliability, and operational cost
Shape SLAs, pricing/packaging inputs for hosted inference, and the operating model for a deployment platform expected to scale quickly
Do whatever work makes deployment succeed — reviewing a serving config, joining an incident retro, inspecting latency data, or writing the rollout plan for a new model
Skills and Qualifications
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

Nice-to-haves

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)
Experience operating in a startup, lab, or new product area where the deployment model and roadmap weren't handed to you
Comfortable moving between a strategic narrative and a specific technical detail (an autoscaling policy, an SLA definition, a rollout gate) without losing judgment
Experience building trust with technical users through evidence, responsiveness, and follow-through rather than process ownership
Location: This role is based in San Francisco, CA.
Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.

Application prep

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Keywords to mirror

aigomlapi

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Pto

Parental Leave