Machine Learning Engineer, Platform

Brain Co. • San Francisco, CA

3d

About The Position

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. 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.

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, documents, and legacy systems.

BrainCo exists to fix that. We build agent-native operating systems for the institutions society depends on, and our products are the first of their kind in the world — we were the first, anywhere, to fully automate construction permitting, and we're now doing the same across insurance and other industries. There is no playbook here, because no one has built this before.

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.

Come help build Atlas - our platform which includes a foundation model for the world's construction documents, extraction agents that read everything from policy stacks to financial filings, a unified eval system across every use case, model routing that puts the right model on the right task at the right cost. You'll own each capability end-to-end — from the pod that needs it this quarter to the abstraction that serves ten pods next year. Your customers are never abstract: they're the project pods building on your work, and through them, every institution we serve.

Requirements

• You understand how machine learning actually works — not just the tooling, but the philosophy underneath: what a loss function really optimizes, how generalization breaks under distribution shift, why evaluation is where systems quietly go wrong.
• You live at the bleeding edge of modern AI, with hard-won instincts for squeezing the most out of LLMs and agentic systems — prompting, fine-tuning, tool use, and reasoning.
• You know when a fine-tuned segmentation model beats a VLM, when a rule engine beats both, and how to compose all three into a system more accurate than any single model.
• You treat frontier models as components to be measured, pushed, and engineered — never as magic.
• You have the platform instinct: you spot the general capability hiding inside three teams' specific requests — and know when generalizing is premature.
• You treat internal teams as real customers with real deadlines, and measure your success in their velocity.
• You'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.

Responsibilities

• Turn pod needs into platform capabilities — find the general capability inside one team's specific, urgent request, without over-abstracting before the pattern is proven.
• Own capabilities end-to-end. There is no handoff: whoever builds the capability owns its behavior in production, across every deployment that uses it.
• Work at the research frontier with production stakes, turning LLMs, RL fine-tuning, and agentic systems into capabilities that dozens of institutional workflows depend on at once.
• Serve customers on both sides of the wall — project pods as true customers, and when needed, the domain experts whose decisions your capabilities ultimately power.
• Engineer for production reality, navigating accuracy, latency, cost, and reliability across environments far messier than any benchmark.
• Raise the bar across the company. The platform is how learnings travel: what one pod discovers, you turn into something every pod inherits.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed