Machine Learning Engineer, Applied AI
Brainco · San Francisco Bay Area
About this role
**Machine Learning Engineer, Applied AI — Brain Co.** **About Brain Co.** Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is industry-specific and 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 and a growing footprint across **government, insurance, health, and financial services**. Every project ships to production and is expected to create measurable customer value and impact. --- ## **About the Role** As a **Machine Learning Engineer on Applied AI**, your work begins where the demo ends: turning impressive models into **production decision systems** that institutions can stake real processes on. You’ll tackle frontier ML applied where it’s hardest—problems that are underspecified, data and documents are messy, and the accuracy bar is **institutional-grade**. Feedback loops are real because our systems move real workflows forward every day. **Examples of work (end-to-end ownership):** - Build **custom vision model pipelines** to check blueprints against building codes (95%+ accuracy) - Create **agents** that untangle policy stacks to reveal coverage gaps - Develop systems that predict from **clinical records** whether a patient is on their care path - Own systems end-to-end—from ambiguous customer problem to the **evaluation** that catches whole classes of errors --- ## **What You’ll Work On** - **Composite AI systems & credit assignment**: chain vision transformers, segmentation models, VLM reasoning, and rule engines—identify which component failed when the pipeline is wrong - **Document understanding beyond the frontier**: build models that can read dense, multimodal documents (blueprints, site plans, policy stacks, contracts, clinical records) - **Agents that learn from real work**: design data, evals, and training loops using verified, ground-truth outcomes from real deployments - **Evaluation as a product discipline**: build eval suites and failure-mode taxonomies rigorous enough for institutional sign-off - **Institutional Intelligence that compounds**: verified corrections improve both the applications and the system that builds the intelligence --- ## **In This Role, You Will** - Turn ambiguity into shipped systems (no problem statement, no labeled data, and no agreed definition of success → well-posed ML + production deployment) - Own AI systems end-to-end (no handoff—training and production behavior are owned by the same person) - Work at the research frontier with production stakes using **LLMs, RL fine-tuning, and agentic systems** - Partner directly with the institutions we serve (permit reviewers, underwriters, compliance officers) to understand how decisions are made - Engineer for production reality: **accuracy, latency, cost, reliability** - Raise the bar across the company via design reviews, internal knowledge sharing, and shared playbooks for AI systems institutions can trust
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