Machine Learning Engineer, Applied AI

What you'll do

• Turn ambiguous customer problems into well-defined ML problems, evaluations, and production deployments.
• Own AI systems end-to-end, including model behavior, production performance, accuracy, latency, cost, and reliability.
• Work with institutional customers and contribute to AI design reviews, the paper club, and shared engineering practices.

What they ask for

• Strong understanding of machine learning, including loss functions, generalization, distribution shift, and evaluation.
• Hands-on expertise with modern AI, including LLMs and agentic systems, prompting, fine-tuning, tool use, and reasoning.
• Ability to build and compose AI approaches such as vision models, segmentation models, VLM reasoning, and rule engines.
• Comfort working on underspecified problems where data and success criteria may need to be developed.

Tools

LLMs, VLM, vision transformers, segmentation models, rule engines, RL fine-tuning

AI focus

Applied AI for institutional workflows, including document understanding, agents trained from verified outcomes, rigorous evaluation, and production decision systems.