AI Platform Engineer Intern (Summer 2027)
Brain Co.
This job at a glance
- Who can apply
- New grads and up to 1 year
- Must know
- Machine learningREST APIsSystem designETLData structures & algorithmsPython
- Work
- On-site in San Francisco Bay Area · internship
- Pay
- Not stated by the employer.
- Skill demand
- REST APIs58%Machine learning54%ETL14%System design12%of 99 similar entry-level jobs posted in the US this week
- Listed
- Oct 30, 2026 · still open
Sign in free to see which of these skills your resume already shows.
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, a proprietary platform designed for control, security, and model flexibility. Brain Co. is entering its next phase of production deployments on a national scale with an elite team and a growing footprint across government, insurance, health, and financial services.
About The Role
As an AI Platform Engineer Intern, you’ll work alongside engineers across Product, ML, and Infrastructure to build the backend services and platform capabilities that power Brain Co.’s AI applications. You’ll contribute to APIs, shared services, developer tooling, and production systems used in enterprise and government environments. This internship is designed for current undergraduate students, specifically juniors and seniors graduating no later than June 2028, who want hands-on experience building real production software. You’ll take on scoped projects across backend systems, distributed services, AI/ML platform integrations, reliability, and developer experience, with mentorship through code reviews, design discussions, and hands-on collaboration with the team.
What You’ll Work On
- Build and improve backend services and APIs—including REST, gRPC, and event-driven systems—that power AI applications and shared platform capabilities.
- Take scoped features from technical design through implementation, testing, deployment, and maintenance.
- Develop modular, reusable platform components and developer-facing tooling that help teams build and ship reliably.
- Work with databases, queues, caches, and distributed systems, learning how to handle consistency, retries, idempotency, and failure modes.
- Improve latency, throughput, reliability, and cost through profiling, observability, and thoughtful system design.
- Contribute to systems that support AI inference, data pipelines, and customer-facing workflows.
- Partner with Product, ML, Infrastructure, and customer-facing teams to translate open-ended needs into clear technical solutions.
- Participate in code reviews, architecture discussions, and technical problem-solving, and see your work deployed to real users.
Qualifications
- Are a current undergraduate junior or senior graduating no later than June 2028 and pursuing a degree in Computer Science, Engineering, or a related technical field.
- Have strong foundations in data structures, algorithms, object-oriented programming, systems, and databases.
- Have experience building software in Go, TypeScript, Rust, Python, Java, C++, or a similar language.
- Have built a backend service, API, data-intensive application, or developer tool through coursework, an internship, research, or a personal project.
- Understand—or are eager to learn—API design, databases, distributed systems, and reliability concepts.
- Are comfortable with Git, testing, and debugging, and are interested in cloud or containerized deployment.
- Enjoy breaking down ambiguous problems from first principles and communicating your approach clearly.
- Are curious, proactive, and excited to own technical problems while seeking feedback when needed.
Internship Details
- Dates: May 31, 2027 - Aug 6, 2027 or July 5, 2027 - September 10, 2027
- Competitive internship 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)