Sr. Model Engineer, RL Data (AI)

Location: Onsite at the Palo Alto, California office

Annual salary: $220,000–$357,000 USD

Company

Builds super-intelligence systems, including frontier coding agents and large-scale reinforcement-learning models trained on real user data.

What you will do

• Design and run rigorous experiments that convert research ideas into measurable model-capability gains.
• Improve data recipes through data-quality analysis, deduplication, and mixture experiments.
• Define high-quality reinforcement-learning data and rewards, including grader design and reward-hacking prevention.
• Advance training science through architecture, scaling, and post-training experiments for coding and computer-use agents.
• Build evaluations and targeted tasks that reveal model weaknesses and guide research priorities.
• Partner with engineers to scale promising results from small experiments to frontier-scale training runs.

Requirements

• 3+ years of hands-on experience training or post-training large neural networks with 1B+ parameters, including pre-training, fine-tuning, or reinforcement-learning methods such as RLHF or DPO.
• Production-quality programming experience in Python, PyTorch, JAX, Rust, or C++.
• Ability to work onsite in person at the Palo Alto office.

Nice to have

• Ability to own ambiguous research tasks and a research direction end to end with little guidance.
• Experience with reinforcement-learning algorithms for language models, scaling laws, or reinforcement-learning environments and evaluations.
• Experience with distributed training at scale, debugging scale-specific problems, and training frontier-scale models.
• Strong foundations in reinforcement learning and machine learning, with excellent software-engineering skills across the research stack.
• Original machine-learning work demonstrated through research, open source, or production impact.
• MS or PhD in computer science, machine learning, mathematics, physics, or a related field, or equivalent research experience.

Culture & Benefits

• Small-team environment with ownership of difficult research problems from hypothesis formation through model integration.
• Medical, vision, and dental coverage.
• 401(k) retirement plan.
• Short- and long-term disability insurance, life insurance, discounts, and other perks.
• Equity compensation.