Role Scope- Build ML and LLM systems that run inside the company's operations: forecasting build timelines, flagging schedule risk, and extracting structure from vendor documents.
- Own models end to end, from problem framing and data through deployment, evaluation, and iteration in production.
- Ship agentic systems with real guardrails, authorization, audit, and evals, so agents act on company systems instead of just advising.
- Partner with data engineering and product pods to put predictions in the tools people already use.
What We're Looking For- The below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.
- You've shipped ML or LLM features to production and owned them after launch.
- You've built evaluation harnesses that told you the truth about model quality before users did.
- You reach for the simplest model that works and can defend the choice.
- You've worked hands-on with LLM APIs, fine-tuning, or retrieval systems on real business problems.
- You write production-quality code and work fluently with AI coding tools.
- Bonus: Forecasting or scheduling problems. Document extraction at scale. Agentic frameworks and MCP. Temporal or workflow engines.
We are committed to pay equity and transparency.
You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you
did not receive a confirmation email, please email [redacted] with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.
