Senior Product Manager, RL Environments — Handshake AI

Handshake Handshake · Enterprise · San Francisco, CA · Product

Senior Product Manager to own the product surface that turns RL environment creation from a bespoke, weeks-long lift into a repeatable factory. This role will design and ship the platform that compresses lead time, replaces hand-built workflows with self-serve tooling, and lets a small team of operators turn out high-quality environments for any vertical.

What you'd actually do

  1. The Environment Factory. The end-to-end product experience for building and shipping an RL environment. Today this is a manual playbook; you’ll define and ship the platform that lets operators run many environments in parallel, with most steps in-product rather than off-platform.
  2. Tooling, packaging, and delivery. Drive the roadmap for the tool registry, environment packaging, and customer delivery so labs receive a portable, deployable environment that runs reliably in their own infrastructure. Reduce time-to-deliver and the rate of last-minute rework on the day of delivery.
  3. Quality at the frontier-lab bar. Own the leveling framework for environment quality (currently L1–L5 by vertical and persona) and the roadmap that gets priority verticals from L1 to L4+. Define and ship the QA tooling that turns environment, task, and rollout QA from a manual review into a productized check.
  4. Operator tooling. Operators are your primary users. Build the dashboards, in-product workflows, and self-serve flows that replace the manual work they do today from data transformation to environment QA to delivery cutoffs.
  5. Goals and metrics. Define and track targets including: environment lead time, environments delivered per quarter, % of in-platform vs. off-platform steps in environment creation, environment quality level by vertical, QA pass rate on environments/tasks/rollouts, tool registry coverage, and operator time per environment.

Skills

Required

  • 5+ years as a product manager shipping production code with engineering teams
  • building tools for internal users
  • owning a product surface with many moving parts and dependencies
  • sequencing roadmap work
  • Strong product instincts in ambiguous, fast-iterating spaces
  • Data-informed and action-oriented
  • Comfortable acting as the connective tissue between Operations and Engineering

Nice to have

  • Background in reinforcement learning, frontier model training data, evaluations, or model post-training workflows
  • Experience as a PM on developer tools or developer platforms
  • Familiarity with data pipelines, de-identification, synthetic data generation

What the JD emphasized

  • reinforcement learning
  • environment creation
  • operator tooling
  • quality
  • platform

Other signals

  • reinforcement learning environments
  • platform for environment creation
  • operator tooling
  • quality at the frontier-lab bar