Member of Technical Staff, Post-Training

About the job

Responsibilities

• Design post-training systems and methodologies for frontier models, including supervised fine-tuning, reinforcement learning, preference optimization, and reward modeling.
• Translate research or partner needs into hypotheses, experiments, evaluation plans, and production-quality implementations.
• Build and improve evaluation frameworks, benchmarks, training environments, data-processing pipelines, and quality-control systems.
• Run iteration loops: prototype, evaluate, interpret results, and implement learnings into 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 the technical bar through design judgment, clear communication, code quality, and mentorship.
• Contribute to the field through benchmarks, open-source tools, research, and technical writing.

Requirements

• 3+ years of demonstrated strength in post-training, fine-tuning, or model-evaluation work (e.g., RL, SFT, LoRA/PEFT, full fine-tuning, RLHF, DPO, PPO, reward modeling, training environments).
• Strong Python skills and ability to write clean, efficient, scalable software.
• Hands-on experience with modern ML tooling, particularly PyTorch and large-scale data, training, or evaluation workflows.
• Sound experimental judgment: ability to form hypotheses, choose metrics, diagnose failures, and distinguish signal from noise.
• Experience designing systems, including making tradeoffs around quality, scale, reliability, and reuse.
• Comfort operating in an ambiguous, fast-moving environment with substantial ownership.
• Collaborative, low-ego communication and ability to work effectively with diverse teams.
• Experience building or operating large-scale ML training, inference, data, or evaluation systems.
• Experience developing LLM/agent benchmarks, evaluation methodologies, annotation systems, or data-quality frameworks.
• Research or applied work on reinforcement learning, alignment, model behavior, synthetic data, or human-in-the-loop systems.
• Published research, meaningful open-source contributions, or evidence of technical leadership in ML systems or AI research.
• Experience productizing research or customer work into robust, reusable platforms.

Benefits

• Equity in a fast-growing company
• 401(k) match
• Competitive compensation
• Financial coaching
• Paid parental leave
• Fertility benefits
• Parental coaching
• Medical, dental, and vision insurance
• Mental health support
• $500 wellness stipend
• $2,000 learning stipend
• Ongoing development
• Commuting support
• Free lunch
• Gym in SF office
• Flexible PTO
• 15 holidays + 2 flex days
• Team outings