Member of Technical Staff

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Member of Technical Staff in USA new

Handshake
Full time
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USA
09/22/2026
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San Francisco, CA

Responsibilities

Design post-training systems and methodologies for frontier models, including supervised fine-tuning, reinforcement learning, preference optimization, and reward modeling.
Translate research and partner needs into hypotheses, experiments, evaluation plans, and production-quality implementations.
Build evaluation frameworks, benchmarks, training environments, data-processing pipelines, and quality-control systems.
Run rapid iteration loops by prototyping, evaluating, interpreting results, and improving systems or products.
Partner with AI researchers and domain experts to develop high-signal data, feedback, and evaluation methods.
Identify repeatable patterns and productize them into reusable software and platforms.
Raise technical standards through design judgment, communication, code quality, and mentorship.
Contribute through benchmarks, open-source tools, research, and technical writing when useful.

Requirements

At least 3 years of demonstrated strength in post-training, fine-tuning, or model-evaluation work.
Relevant experience may include reinforcement learning, supervised fine-tuning, LoRA/PEFT, full fine-tuning, RLHF, DPO, PPO, reward modeling, or training environments.
Strong Python skills and the ability to write clean, efficient, scalable software.
Hands-on experience with modern ML tooling, particularly PyTorch and large-scale data, training, or evaluation workflows.
Strong experimental judgment, including forming hypotheses, selecting metrics, diagnosing failures, and distinguishing signal from noise.
Experience designing systems and making tradeoffs involving quality, scale, reliability, and reuse.
Ability to operate with substantial ownership in an ambiguous, fast-moving environment.
Collaborative communication and ability to work effectively with researchers, engineers, domain experts, and customers.
Preferred experience includes large-scale ML training, inference, data, or evaluation systems; LLM or agent benchmarks; annotation or data-quality frameworks; reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems; published research; open-source contributions; technical leadership; or productizing research into reusable platforms.

Benefits

Equity in a fast-growing company.
401(k) match, competitive compensation, and financial coaching.
Paid parental leave, fertility benefits, and parental coaching.
Medical, dental, vision, mental health support, and a $500 wellness stipend.
A $2,000 learning stipend and ongoing development.
Commuting support, free lunch, and gym access at the San Francisco office.
Flexible PTO, 15 holidays, and 2 flex days.
Team outings and referral bonuses.
Full-time US employee benefits apply; San Francisco and Mountain View are preferred, with exceptional candidates considered in other locations.

Salary: $200k - $350k/yr

Handshake

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