Machine Learning Engineer, Applied AI

About the role

Brain Co. builds AI‑native operating systems for regulated institutions. As a Machine Learning Engineer on Applied AI you will take models from impressive demos to production decision systems that drive real institutional workflows.

Key responsibilities

• Design, develop, and ship end‑to‑end AI systems that process multimodal documents such as blueprints, policy stacks, and clinical records.
• Own the full ML lifecycle: define ambiguous problems, create labeled data, build evaluation suites, and maintain model behavior in production.
• Compose composite AI pipelines that combine vision transformers, segmentation models, VLM reasoning, and rule engines, and perform credit assignment when pipelines fail.
• Collaborate directly with domain experts (permit reviewers, underwriters, compliance officers) to ensure models meet institutional accuracy, latency, cost, and reliability requirements.

Required profile

• Deep understanding of machine‑learning fundamentals, loss functions, distribution shift, and rigorous evaluation.
• Experience with cutting‑edge AI, especially large language models, vision transformers, and agentic systems, and the ability to fine‑tune and prompt them effectively.
• Comfort building novel solutions where problem statements, data, and success metrics must be invented from scratch.

Required skills

• Machine learning model development and production deployment.
• Large language models (LLMs) and vision transformers.
• Segmentation models and visual‑language models (VLMs).
• Rule‑engine integration and composite AI pipeline construction.