AI Platform Engineer Co-Op Intern, Winter 2027

Our Mission

Rebuild how the world works, to make institutions work better for the people they serve.

About Brain Co.

Brain Co. builds AI-native operating systems for large, regulated institutions. Each system is built for a specific industry, powered by agents that push real workflows forward. Underneath it all is Atlas, our proprietary platform that keeps customers in control, secure by design, and never locked into one model.

Why Now

Brain Co. is entering its next phase of production deployments on a national scale with an elite team built from Palantir, Google, Meta, and Nvidia, and a growing footprint across government, insurance, health, and financial services.

Joining now means shaping both the company and a new category of applied AI. Every project here ships to production and is expected to create measurable customer value and impact.

You'll work alongside exceptional peers on some of the hardest problems in applied AI. It’s the kind of work you'll still be proud of in ten years from now.

About the Co-Op

As an AI Platform Engineer Co-Op Intern , you’ll work alongside experienced platform and infrastructure engineers to help build the systems that power Brain Co.’s AI applications in production.

This internship is designed for current undergraduate students ( junior or senior ) who want hands-on experience contributing to real production systems, while receiving mentorship through code reviews, design discussions, and hands-on collaboration with the team. You’ll gain hands-on experience with cloud infrastructure, Kubernetes, distributed systems, observability, and the infrastructure behind modern AI workloads.

What You’ll Work On

• Help build and improve Kubernetes- and Terraform-based infrastructure across production and customer environments.

• Contribute to tooling and automation that makes infrastructure easier and safer for engineers to deploy and operate.

• Work with cloud infrastructure, networking, CI/CD, and containerized systems .

• Help build and improve metrics, logging, tracing, dashboards, and monitoring for production services.

• Investigate performance and reliability issues across distributed systems and AI workloads.

• Support infrastructure for AI inference, data pipelines, and large-scale backend services .

• Participate in code reviews, architecture discussions, and technical problem-solving alongside experienced engineers.

• See the infrastructure you build support real applications used by customers at scale.

You Might Be a Great Fit If You…

• Are a current undergraduate student , specifically a junior or senior , pursuing a degree in Computer Science, Machine Learning, Data Science, or a related field

• Have strong software engineering fundamentals and experience programming through coursework, internships, research, or personal projects.

• Have experience with a programming language such as Python, Go, Java, C++, or similar .

• Have exposure to or an interest in cloud infrastructure, distributed systems, operating systems, networking, or databases .

• Are familiar with or excited to learn technologies like Kubernetes, Terraform, Docker, AWS/GCP/Azure, and observability tools .

• Enjoy understanding how systems work under the hood and solving problems involving performance, reliability, and scale .

• Are curious, proactive, and excited to learn quickly in a fast-paced engineering environment.

• Enjoy taking ownership of technical problems while learning from experienced engineers.

What You’ll Gain

Co-Op Details & Benefits

• Winter / Spring Co-Op: January 11, 2027 - April 30, 2027

• Competitive co-op compensation

• Hands-on mentorship from experienced AI engineers and researchers

• Opportunity to work on real production systems used globally

• Potential return-offer consideration for future internships or full-time roles

• Daily lunches (if onsite)

Compensation

The base salary range for this internship role is $8,000-10,000 per month plus a stipend for housing . We publish this range for every internship role, as a matter of policy regardless of whether local law requires it. Actual pay will depend on scope, skills, experience.