Member of Technical Staff - Reinforcement Learning

Member of Technical Staff - Reinforcement Learning Optimized deploys AI agents into the most critical supply chains in the world: the operations of Fortune 500 manufacturers and government organizations. Those agents have to make sound decisions across long, multi-step workflows where a single misstep is costly, and we need a reinforcement learning researcher to teach them how. As a Member of Technical Staff on reinforcement learning, 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 your work in real deployment data and ship it into production, closing the loop between how agents learn and how they perform in the field. Compensation range for this role is $185,000 - $255,000 + equity. What you'll do • Train agents to act: You'll design and run RL and post-training pipelines that improve how our 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. What you'll bring • 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 #J-18808-Ljbffr