Research Engineer - Agents

Optimized deploys AI agents into the most critical supply chains in the world: the operations of Fortune 500 manufacturers and government organizations. Getting a promising research idea to run reliably in those environments is its own hard problem, and we need a research engineer to own the path from prototype to production. As a Research Engineer on agents, you'll work at the seam between research and engineering, building the training stack, evaluation harnesses, and inference and serving systems that move ideas from notebook to deployment. You'll turn fragile prototypes into production agent capabilities customers depend on, and turn production signals back into the next round of research. Compensation range for this role is $175,000 - $240,000 + equity. What you'll do Bridge research and production: You'll take promising research ideas and turn them into robust, production-ready agent capabilities customers depend on. Build the training stack: You'll own the data pipelines, fine-tuning, and experiment infrastructure that let researchers iterate quickly. Make evaluation fast: You'll build the eval harnesses and tooling that measure whether a change actually improves agent quality. Optimize for production: You'll drive down latency and cost across model providers without sacrificing reliability. Close the loop: You'll turn production signals and failures back into the next round of research and improvement. What you'll bring Have 3+ years building production ML or AI systems, including owning models from prototype to deployment Have hands‑on experience with LLMs (fine‑tuning, retrieval‑augmented generation, and evaluation) Are fluent in Python and modern ML tooling (PyTorch, HuggingFace, or similar) Are rigorous about evaluation, and you measure before you optimize Enjoy working at the seam between research and engineering Are excited about applying AI to real‑world industrial problems, not just benchmarks #J-18808-Ljbffr