Machine Learning Engineer, Applied AI

Brain Co. is hiring a Machine Learning Engineer, Applied AI to build production decision systems for regulated institutions. The role spans ambiguous customer problems through model development, evaluation and deployment, with applications in government, insurance, healthcare and financial services. Examples include vision pipelines that check blueprints against building codes at 95%+ accuracy, agents that identify gaps in insurance policy stacks, and systems that assess care paths from clinical records.

Responsibilities

• Define well-posed ML problems when requirements, labeled data and success criteria are initially unclear; own system behavior after deployment rather than handing off a trained model.
• Build composite systems combining vision transformers, segmentation models, vision-language models, LLM reasoning and rule engines. Diagnose which component caused a pipeline failure and choose the approach best suited to each task.
• Develop approaches to understanding dense, multimodal documents such as blueprints, site plans, policies, contracts and clinical records.
• Use verified outcomes from deployments to design data, evaluation and training loops, including fine-tuning agents and applying reinforcement-learning fine-tuning where appropriate. Build evaluation suites and failure-mode taxonomies rigorous enough for institutional review.
• Work directly with permit reviewers, underwriters and compliance officers to understand decisions and improve workflows. Balance accuracy, latency, cost and reliability, and contribute through design reviews and the company’s shared AI-systems practices.

Qualifications

The posting seeks a strong understanding of ML fundamentals, including loss functions, generalization under distribution shift and evaluation, alongside practical judgment with LLMs and agentic systems, prompting, fine-tuning, tool use and reasoning. Candidates should be comfortable defining problems, data and measures of success in unfamiliar settings. No specific degree or years of experience are stated.

This is a full-time, hybrid role listed for the San Francisco Bay Area or New York City, NY.