2 months ago
Responsibilities
- Turn project-pod needs into reusable platform capabilities.
- Own shared ML capabilities end-to-end across every production deployment that uses them.
- Build document extraction agents, foundation models, model routing, and unified evaluation systems.
- Develop composite AI systems and diagnose which component caused failures.
- Capture production corrections and turn them into retraining and safe redeployment across use cases.
- Engineer for production accuracy, latency, cost, and reliability while sharing learnings across teams.
Requirements
- Strong understanding of machine-learning fundamentals, including loss functions, generalization, distribution shift, and evaluation.
- Hands-on expertise with LLMs and agentic systems, including prompting, fine-tuning, tool use, and reasoning.
- Ability to select and compose models, vision-language models, and rule engines based on accuracy, cost, latency, and reliability.
- Platform-oriented judgment to identify reusable capabilities without over-abstracting prematurely.
- Comfort building novel systems where the problem, data, and success criteria are not fully defined.
- Ability to own capabilities through production and serve project pods and domain experts as customers.
Categories
About Brain Co.
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