Machine Learning Engineer

About the Role

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

• 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.

Qualifications

• Bonus: Forecasting or scheduling problems.
• Document extraction at scale.
• Agentic frameworks and MCP.
• Temporal or workflow engines.

Pay

Compensation Range: $224K - $279K

Fluidstack is committed to pay equity and transparency.