You've personally fine-tuned models and shipped the result into something real
Experience with ML platform, MLOps, or AI infrastructure products — training platforms, eval tooling, or model registries
Familiarity with reinforcement fine-tuning specifics: reward modeling, rollout environments, and agent-training workflows
Understanding of GPU economics and how training cost, throughput, and quality trade off against each other
Open-source or developer-community experience — you know what makes an SDK or API feel good to use
Early startup or founding experience
2 – 8+ years of product management experience building technical or developer-facing products (we are hiring at multiple levels for this role)
Strong technical background — CS/EE degree, production engineering experience, or equivalent depth earned on the job
Familiarity with the post-training lifecycle: dataset curation, SFT, LoRA/PEFT, RL-based methods, evaluation, and how these connect to inference in production
Demonstrated ownership of a product area end to end from strategy, spec, launch, to metrics
Excellent written communication. You can write a spec, a launch post, and a customer-facing explanation of a tradeoff, and all three will be clear
Comfort with ambiguity, and a bias toward shipping and learning over waiting for certainty
Deep hunger and motivation. This isn't a 9-5 job and you'll be expected to step up, especially during periods of "wartime."