Principal Product Manager

We're looking for a Principal Product Manager to sit inside that process. You'll be embedded directly with applied researchers and ML engineers running RLHF, fine-tuning, and preference-tuning pipelines, reading model outputs, calling out what's wrong, and turning that into concrete training priorities. You're the person who brings the end user's voice into a room that's otherwise all metrics and loss curves.

This is a technical, hands-on PM role, not just a roadmap-and-requirements role. You'll spend real time in transcripts and eval dashboards, and you'll need enough grounding in how post-training actually works (RLHF, DPO/preference optimization, instruction tuning, data curation) to have a credible point of view with the research team.

Embed in the post-training loop: work day-to-day with research and applied ML teams during fine-tuning and RLHF cycles, reviewing model outputs and giving structured feedback on quality, tone, and behavior

Define what "good" means for Copilot's models: set the priorities for capability, personality, and safety tradeoffs across surfaces (Microsoft 365 Copilot, Copilot Studio agents, Windows) — where "good" is often genuinely ambiguous and hasn't been decided before

Run RLHF/preference data campaigns: partner with data and labeling teams to scope what human preference data gets collected, for which behaviors, and why

Build and maintain behavior evals: translate qualitative judgment calls ("this response felt too hedgy," "this refused when it shouldn't have") into evaluation sets that can be tracked release over release

Own the feedback loop: turn user research, enterprise customer feedback, and production incident learnings into concrete post-training priorities — closing the gap between "users are unhappy with X" and "the next fine-tune addresses X"

Make the tuning tradeoffs explicit: helpfulness vs. caution, consistency vs. personality, latency/cost vs. quality — and drive alignment across research, safety, and product leadership on where the line sits

Represent Responsible AI and enterprise requirements into training priorities — compliance behaviors, refusal policies, and brand voice all have to be reflected in what the model is actually tuned to do.