11 hours ago
Responsibilities
- Turn ambiguous customer problems, unlabeled data, and unclear success criteria into well-defined ML problems and shipped production systems.
- Own AI systems end-to-end, including model training, evaluation, deployment, and production behavior.
- Build and fine-tune composite AI systems using vision transformers, segmentation models, VLM reasoning, LLMs, agents, and rule engines.
- Design data, evaluation suites, failure-mode taxonomies, and training loops using verified outcomes from real deployments.
- Work directly with permit reviewers, underwriters, compliance officers, and other institutional users to improve decision workflows.
- Engineer for production constraints including accuracy, latency, cost, and reliability.
- Contribute to design reviews, the internal paper club, and shared practices for trustworthy institutional AI.
Requirements
- Strong understanding of machine-learning fundamentals, including loss functions, generalization, distribution shift, and evaluation.
- Hands-on experience with modern AI systems, including prompting, fine-tuning, tool use, reasoning, LLMs, and agentic systems.
- Ability to determine when different approaches, such as fine-tuned models, VLMs, and rule engines, are appropriate and compose them into reliable systems.
- Ability to work independently on underspecified problems and invent the data, objectives, and evaluation methods needed for success.
- Ability to own ML systems from research and model development through production deployment and institutional use.
Categories
About Brain Co.
We're building an AI platform and applications for the world's most important institutions. Learn more at https://brain.co/