Director, Applied Machine Learning at Handshake
San Francisco, CA
$375,000–$400,000
Jul 13, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
A senior leadership role overseeing applied machine learning work at the intersection of frontier AI research and large-scale delivery. The director will own post-training and reinforcement learning environments, the infrastructure to run them at scale, and strategic engagement with AI lab partners. The role involves building and managing a team of AI engineers and research scientists, while maintaining direct relationships with technical clients and translating ambiguous priorities into clear execution.
Principal level · San Francisco-Oakland-Berkeley, CA · Full-time
“or” means any one of them counts — you don't need all of them.
Posted 3 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Rare in this occupation — lead with these, and say what you built with them.
What the occupation pays Median $238,011 (middle half $208,323–$320,108).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
This is a Director-level people-management role (managing AI Engineers, Research Scientists, and Forward Deployed Engineers, with a path to managing managers), which maps primarily to 11-3021. However, the role carries significant hands-on technical depth expectations in applied ML and post-training, making 15-2051 a plausible runner-up.
The title 'Director of Applied ML' carries Director/Principal-level framing; the scope (org-wide technical and strategic ownership, managing managers, direct lab partnerships) supports a Principal-band classification.
No compensation figures are disclosed in the posting.
The role is hybrid (3 days/week in-office in San Francisco) — not fully remote.
PPO is listed as the primary named algorithm; GRPO and SFT are listed as alternatives in the same 'or similar' clause in the requirements section.
Evaluation frameworks, annotation tooling, and RLHF/human feedback collection at scale appear only under the 'Extra Credit' (nice-to-have) section.
No minimum years of experience or degree requirement is stated anywhere in the posting.
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): AI systems design.
Read the full posting
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