Product Manager - Post Training

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

This is a product role for someone who can operate inside frontier research without trying to turn research into a conventional software roadmap. The work requires judgment, technical fluency, user empathy, discretion, and the ability to create clarity without creating bureaucracy.

The Post-Training Product Manager will work as a high-trust partner to our post-training researchers. Your role is to understand the work deeply enough to ask the right questions, identify missing connections, surface implications, and help the team decide what matters next. The role sits at the seam between research, model behavior, data and environments, evaluations, training and inference infrastructure, safety, product, and the people using our models.

Inkling was post-trained across math, agentic code and tool use, audio, image, chat, and safety, with a large-scale asynchronous RL program that exceeded 30M rollouts. Inkling and Inkling-Small share a scalable post-training stack, and Inkling is available for customization on Tinker.

The next phase requires tight loops among research priorities, model behavior, evaluations, infrastructure constraints, user and customer learning, and product direction. The person in this role will help those loops compound instead of fragment — maintaining the bird's-eye view while researchers go deep: where work is converging, where teams are solving adjacent problems without enough shared context, what evidence is missing, what decisions are blocked, and how research becomes a stronger model and a useful product.

What You'll Do

- Partner with post-training research leaders on priorities, sequencing, decision points, and the connection between research work and the broader model and product agenda

- Maintain a clear view across SFT, RL, data and environments, evaluations, safety, model behavior, inference, training infrastructure, and product dependencies; identify gaps before they become blockers

- Translate ambiguous research and product questions into concrete learning plans: what must be true, what evidence would change the decision, which experiments or user signals matter, and when the team should revisit the direction

- Build lightweight operating mechanisms for critical work — owners, state, dependencies, decisions, risks, release criteria, and follow-through — without imposing a software-development process on research

- Bring qualitative and quantitative evidence about model behavior and real workflows into research prioritization, ensuring user knowledge is represented accurately rather than flattened into feature requests

- Connect research, product, infrastructure, safety, and leadership when a decision spans teams or when local optimization creates a broader product or model tradeoff

- Support the path from research result to usable capability: internal adoption, evaluation, documentation, release readiness, product integration, and feedback after launch

- Write clear narratives that explain what the team has learned, what remains uncertain, what decisions are needed, and why the work matters

- Take on the unowned work that is necessary to move a critical research-product outcome forward

Skills and Qualifications

Minimum qualifications:

- Experience as an early or first product leader in a technical startup, AI lab, research organization, or new product area where the role and operating model were not defined for you

- Experience working closely with model training, post-training, RL, evaluations, data, safety, inference, developer platforms, or another technically demanding research-product area …