Machine Learning Engineer, Applied AI

Our Mission Rebuild how the world works, to make institutions work better for the people they serve. About Brain Co. 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. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model. Why Now Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services. Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact. You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now. Machine Learning Engineer, Applied AI About the Role So much of the work society depends on is still slower and harder than it should be. Permits take months. Claims sit unresolved. And AI hasn't changed that — because the bottleneck isn't the models. It's the institutional context AI needs to do the work: rules, history, relationships, and judgment scattered across people, d [... source excerpt omitted ...] no one has built this before. As a Machine Learning Engineer on Applied AI, your work begins where the demo ends: getting a model to look impressive is the easy part; making it a production decision system an institution stakes its process on is the job. The problems come in every shape — custom vision model pipelines that check blueprints against building codes at 95%+ accuracy, agents that untangle policy stacks to reveal coverage gaps, systems that predict from clinical records whether a patient is on their care path — and you'll own them end-to-end, from ambiguous customer problem to the eval that catches a whole class of errors. This is frontier ML applied where it's hardest [... source excerpt omitted ...] u're energized by building things that have never existed, and comfortable when the problem, the data, and the definition of success all have to be invented at once. The Problems You'll Work On Composite AI systems and credit assignment. Our most demanding systems chain vision transformers, segmentation models, VLM reasoning, and rule engines. When the pipeline is wrong, which component failed? One of the most interesting open problems in applied ML. Document understanding beyond the frontier. Blueprints, site plans, policy stacks, contracts, clinical records — dense, multimodal documents that break off-the-shelf models. You'll build models that actually read them. Agents t