Machine Learning Engineer, Platform

Brain Co. builds AI-native operating systems for large, regulated institutions. As a Machine Learning Engineer on Platform, you'll build the core ML capabilities every product we ship stands on — built once, shared everywhere. This is the leverage seat in the company: improve document extraction, and every vertical improves with it; strengthen the blueprint foundation model, and every construction workflow gets sharper; ship a better improvement loop, and every system we've ever deployed keeps getting better on its own.

Responsibilities

Turn pod needs into platform capabilities; Own capabilities end-to-end, including behavior in production across every deployment; Work at the research frontier with production stakes, turning LLMs, RL fine-tuning, and agentic systems into capabilities; Serve customers on both sides of the wall, including project pods and domain experts; Engineer for production reality across accuracy, latency, cost, and reliability; Raise the bar across the company by turning pod discoveries into shared capabilities

Requirements

Deep understanding of how machine learning works, including loss functions, generalization under distribution shift, and evaluation; Experience at the bleeding edge of modern AI with LLMs and agentic systems, including prompting, fine-tuning, tool use, and reasoning; Ability to know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose them; Treat frontier models as components to be measured, pushed, and engineered; Platform instinct to spot general capabilities across teams' requests and avoid premature generalization; Treat internal teams as real customers with real deadlines; Energized by building things that have never existed and comfortable inventing the problem, data, and definition of success