Director, Applied Machine Learning

Handshake•San Francisco, CA

1d•$375,000 - $400,000•Hybrid

About The Position

As Director of Applied ML, you'll lead the function that sits at the intersection of frontier AI research and enterprise-scale delivery. You'll own three core pillars: post-training and RL environments, scaled post-training infrastructure, and strategic project support across our lab partnerships. You think like an applied researcher, lead like an operator, and move fluidly between technical depth and client-facing strategy. You'll build and grow a team, set the technical standards for how the team engages with frontier labs, and help shape the roadmap as we scale, not just execute against it. This is a rare opportunity to lead at the frontier of gen AI, with direct visibility into where model training is headed before the rest of the world sees it.

Requirements

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, though you don't need to have trained frontier models yourself
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 Haves

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

Responsibilities

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
Build and scale the infrastructure and team needed to run post-training at scale across multiple applied use cases
Own strategic project support across lab engagements — leading your team through a dynamic, evolving roadmap rather than executing the work solo
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
Partner directly with AI lab researchers and internal stakeholders across engineering and operations, translating ambiguous priorities into clear direction for your team
Maintain direct customer relationships, following up, communicating progress, and building trust with technical partners
Bring organizational maturity to a fast-growing team: solve today's problems with today's resources while planning several steps ahead
Stay current on the frontier: RL, post-training, and evaluation methods, bringing relevant insight into both team strategy and customer conversations