Machine Learning Engineer
Build and own ML/LLM systems for internal operations including forecasting, risk flagging, and document extraction. Ship production agentic systems end-to-end with guardrails and partner with data engineering to integrate predictions into tools.
About the job
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.
Requirements
- Shipped ML or LLM features to production and owned them after launch.
- Built evaluation harnesses that told you the truth about model quality before users did.
- Reach for the simplest model that works and can defend the choice.
- Worked hands-on with LLM APIs, fine-tuning, or retrieval systems on real business problems.
- Write production-quality code and work fluently with AI coding tools.
Nice-to-Haves
- Forecasting or scheduling problems.
- Document extraction at scale.
- Agentic frameworks and MCP.
- Temporal or workflow engines.
Skills
Machine Learning, LLMs, LLM APIs, Fine-Tuning, Retrieval Systems, Evaluation Harnesses, Production Ml, Agentic Systems, Python
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