Machine Learning Engineer, Applied AI

Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. As a Machine Learning Engineer on Applied AI, the work begins where the demo ends: turning models into production decision systems that institutions rely on. Problems include custom vision model pipelines that check blueprints against building codes, agents that untangle policy stacks to reveal coverage gaps, and systems that predict from clinical records whether a patient is on their care path.

Responsibilities

Turn ambiguity into shipped systems, from no problem statement, no labeled data, and no agreed definition of success, to well-posed ML problems and production deployments; Own AI systems end-to-end, including owning model behavior in production; Work at the research frontier with production stakes, applying LLMs, RL fine-tuning, and agentic systems where the output is a decision an institution acts on; Work directly with institutions such as permit reviewers, underwriters, and compliance officers to understand how decisions are made; Engineer for production reality, balancing accuracy, latency, cost, and reliability; Raise the bar across the company through design reviews, an internal paper club, and a shared playbook for AI systems institutions can trust

Requirements

Understanding of how machine learning works, including loss functions, generalization under distribution shift, and evaluation; Hands-on experience with LLMs and agentic systems, including prompting, fine-tuning, tool use, and reasoning; Ability to treat frontier models as components to be measured and engineered; Comfort building new systems when the problem, data, and definition of success must be invented