Brainco
Machine Learning Engineer, Platform
San Francisco Bay Area
Sponsorship not specifiedDetected 80 days ago
Machine LearningLLMsAgentic AIFinancial AnalysisResearchFinancial Reporting
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odds of building a lasting career here
37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
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from this employer's Department of Labor filings
Files H-1B transfers
7 transfer filings in the last year, covering 7 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
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About the role
- So much of the work society depends on is still slower and harder than it should be.
- And AI hasn't changed that - because the bottleneck isn't the models.
- It's the institutional context AI needs to do the work:
Responsibilities
- Own capabilities end-to-end.
- whoever builds the capability owns its behavior in production, across every deployment that uses it.
- Brain Co. builds AI-native operating systems for large, regulated institutions.
Company info
- 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.
- 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.
- And 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.
- That combination is the job: 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 also 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.
- Most of all, 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.
- Agents as shared capabilities.
- Document extraction, financial reporting, market data - built once, composed into many products.
- The dual bar: general enough for any pod to pick up, precise enough for decisions institutions stake their processes on.
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This listing is sourced directly from Brainco's careers page and normalized into a canonical job model.