Director, Applied Machine Learning

Handshake Handshake · Enterprise · San Francisco, CA · HAI Research

Director of Applied ML to lead post-training and RL environments, scaled post-training infrastructure, and strategic project support for frontier AI labs. This role involves building and growing a team, setting technical standards, and shaping the roadmap for a fast-growing AI data business.

What you'd actually do

  1. Lead post-training and RL environment strategy — you don't need to be the deepest technical expert, but your team needs to be able to lean on you for real guidance grounded in lab experience
  2. Build and scale the infrastructure and team needed to run post-training at scale across multiple applied use cases
  3. Own strategic project support across lab engagements — leading your team through a dynamic, evolving roadmap rather than executing the work solo
  4. Manage and grow a team including AI Engineers, Research Scientists and AI Forward Deployed Engineers, with a path toward managing managers as the org scales
  5. Partner directly with AI lab researchers and internal stakeholders across engineering and operations, translating ambiguous priorities into clear direction for your team

Skills

Required

  • Experience leading and growing technical teams, ideally with a research background in applied ML or AI
  • Technical depth in applied AI systems design and model post-training (GRPO/PPO, SFT, or similar) enough to support a research-leaning team
  • Experience building and scaling teams or infrastructure in an applied, production environment
  • Strong cross-functional leadership, comfortable working across engineering and operations, and managing shifting timelines with a team in tow
  • Excellent client-facing communication skills — you can translate technical work into business impact and hold your own with both researchers and customers
  • Organizational maturity and comfort with ambiguity — you'll need to be creative in solving problems as they arrive

Nice to have

  • Prior lab experience deploying or training agents for applied, real-world use cases
  • Experience managing managers or scaling a team through multiple growth stages
  • Familiarity with evaluation frameworks, annotation tooling, or human feedback collection at scale

What the JD emphasized

  • post-training
  • RL environments
  • scaled post-training infrastructure
  • strategic project support
  • applied ML
  • model post-training
  • building and scaling teams or infrastructure
  • managing shifting timelines
  • organizational maturity
  • comfort with ambiguity

Other signals

  • leading frontier AI research and enterprise-scale delivery
  • post-training and RL environments
  • scaled post-training infrastructure
  • strategic project support across our lab partnerships
  • direct visibility into where model training is headed