Research Scientist - Reinforcement Learning

We're building a small, talent-dense team to deploy AI agents into the most critical supply chains in the world.

You'll own RL and post-training: the reward models, training loops, and evaluations that turn raw model capability into reliable long-horizon decision-making. You'll ground that work in real deployment data and ship it into production.

This role is on-site 5 days a week in San Francisco. Visa transfers (OPT, H-1B) are supported. Compensation range is $185,000 - $255,000 + competitive equity.

• Train agents to act:
  You'll design and run RL and post-training pipelines that improve how the agents plan and execute multi-step work.
• Build reward models:
  You'll define and train the reward signals that capture what a good supply chain decision actually looks like.
• Evaluate long-horizon behavior:
  You'll build evals that measure agent reliability across long, high-stakes workflows, not just single turns.
• Ground learning in reality:
  You'll use real deployment data and feedback to close the gap between simulation and production.
• Ship research to production:
  You'll work with engineers to bring training breakthroughs into the live agent platform.

• Have a PhD or equivalent research experience in RL, ML, or a related field
• Have hands-on experience with reinforcement learning, post-training, or RLHF for LLMs
• Are comfortable building research prototypes in Python and iterating quickly
• Understand reward modeling, policy optimization, and evaluation of sequential decision-making
• Care about real-world impact, and you have driven research through to production
• Are excited about applying AI to complex, messy, real-world optimization problems