Applied AI ML Engineer — Production Systems in Institutions

Brain Co.; New York (NY); On-site; USD 150,000 - 190,000; Full time

Job summary

Brain Co. builds AI-native operating systems for large, regulated institutions, delivering production-ready AI that scales across industries such as government, insurance, health, and financial services.

As a Machine Learning Engineer on Applied AI, you will own end-to-end ML systems, from data to production, including prompts, fine-tuning, and reasoning with cutting-edge agents. You will work on composite AI suites that reason across documents and policies, and you will shape how institutions

Qualifications

• You understand how machine learning actually works — not just the tooling, but the philosophy underneath.
• You live at the bleeding edge of modern AI, with instincts for squeezing value from LLMs and agentic systems.
• You can design, implement, and productionize ML systems that make institutional processes faster and more reliable.

Responsibilities

• Turn ambiguity into shipped systems from no label data to production deployments.
• Own AI systems end-to-end with no handoff between training and production.
• Work with institutions to understand decision making and ensure systems change how work gets done.
• Engineer for production reality: manage accuracy, latency, cost, and reliability.

Skills

ML fundamentals

LLMs

Agentic systems

Fine-tuning

Prompting

Production ML

Python

Tools

PyTorch

TensorFlow

Hugging Face

Job description

Brain Co. builds AI-native operating systems for large, regulated institutions, delivering production-ready AI that scales across industries such as government, insurance, health, and financial services.

As a Machine Learning Engineer on Applied AI, you will own end-to-end ML systems, from data to production, including prompts, fine-tuning, and reasoning with cutting-edge agents. You will work on composite AI suites that reason across documents and policies, and you will shape how institutions