BrainCo
Machine Learning Engineer, Platform
About this role
Brain Co. seeks an ML Engineer for Platform to build core, reusable machine learning capabilities underpinning AI-native operating systems for regulated institutions. You'll design and ship foundation models, extraction agents, evaluation systems, and model routing infrastructure that serve multiple product teams while solving novel problems in applied AI at scale.
What you'll do
- Design and develop shared ML capabilities (document extraction, foundation models, evaluation systems) used across multiple product verticals
- Build and improve a foundation model specialized for construction documents and institutional workflows
- Create model routing and selection systems to optimize cost, latency, and accuracy across frontier and fine-tuned models
- Develop feedback loops and improvement mechanisms so deployed systems continuously learn from verified corrections
- Collaborate with product pods to identify generalizable patterns and abstractions from specific use cases
- Own ML components end-to-end from prototype through production deployment at institutional scale
What they're looking for
- Deep understanding of machine learning fundamentals (loss functions, generalization, distribution shift, evaluation)
- Hands-on experience with LLMs, prompt engineering, fine-tuning, and agentic systems
- Expertise in model evaluation and metrics design across diverse use cases
- Experience building and deploying document understanding and extraction systems
- Proficiency in composing multiple model types (fine-tuned, foundation, rule-based) into unified systems
- Platform engineering mindset with ability to build reusable, general-purpose ML infrastructure
- Python and ML frameworks (PyTorch, TensorFlow, or equivalent)
- Understanding of production ML systems and real-world deployment constraints
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BrainCo
BrainCo builds and deploys cutting-edge AI and language model solutions for governments, healthcare systems, and critical infrastructure organizations. The company is hiring AI/ML engineers, backend platform engineers, AI platform engineers, and sales engineers to develop scalable infrastructure, production AI systems, and secure government contracts.
View all jobs at BrainCoLikely interview questions
- Describe a time you chose between fine-tuning a model versus using a foundation model versus implementing rule-based logic—what factors drove your decision?
- How would you approach building a single evaluation system that meaningfully measures quality across fundamentally different ML tasks (e.g., document segmentation and financial reporting)?