Model Engineer (AI)

Location: Onsite in Palo Alto, California, United States; must be able to work in person at the Palo Alto office.

Annual salary: $160,000–$250,000 USD

Company

Builds super-intelligence systems, frontier coding agents, and large-scale model applications.

What you will do

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

Requirements

• Production-quality programming experience in Python, PyTorch, JAX, Rust, or C++.
• Ability to handle ambiguous research tasks with little guidance and own a research direction end to end.
• Experience with reinforcement-learning algorithms for language models, scaling laws, or reinforcement-learning environments and evaluations.
• Experience with distributed training at scale and debugging issues that appear only at scale.
• Strong foundations in reinforcement learning and machine learning, with hands-on experience training large models through pretraining, post-training, or reinforcement learning.
• Excellent programming and software-engineering skills across the research stack, especially Python and PyTorch or JAX.
• Track record of 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.

Nice to have

• Experience training frontier-scale models.
• Unusually strong attention to data quality and willingness to investigate data directly.
• Truth-seeking approach focused on learning about the science rather than proving an idea correct.

Culture & Benefits

• Small-team environment with end-to-end ownership of ambiguous, difficult research problems.
• Medical, vision, and dental coverage.
• 401(k) retirement plan.
• Short- and long-term disability insurance, life insurance, discounts, perks, and equity.