Machine Learning Engineer, Platform

Responsibilities

• Turn urgent project-pod needs into reusable platform capabilities without over-abstracting before patterns are proven.
• Build and own shared ML capabilities for document extraction, institutional intelligence, foundation models, model routing, and evaluation.
• Develop LLM, fine-tuning, and agentic-system capabilities for production institutional workflows.
• Engineer composite AI systems combining vision models, VLM reasoning, and rule engines, including component-level credit assignment.
• Build continuous-improvement machinery that captures production corrections, triages failures, supports retraining, and enables safe redeployment.
• Balance accuracy, latency, cost, and reliability across production environments while serving project pods and domain experts.

Requirements

• Strong understanding of machine learning fundamentals, including loss functions, generalization, distribution shift, and evaluation.
• Experience working with modern AI systems, including prompting, fine-tuning, tool use, reasoning, LLMs, and agentic systems.
• Ability to determine when to use fine-tuned segmentation models, VLMs, rule engines, or combinations of these approaches.
• Platform-oriented judgment and the ability to identify general capabilities in team-specific requests.
• Ability to work effectively with ambiguous problems, evolving data, and newly defined success criteria.
• Ability to own ML capabilities end-to-end and operate them in production.