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About the Role
As an AI Platform Engineer Intern, you will collaborate with engineers across Product, ML, and Infrastructure to develop backend services and platform capabilities for Brain Co.'s AI applications. You will contribute to APIs, shared services, developer tooling, and production systems for enterprise and government use. This internship is for current undergraduate juniors and seniors graduating by June 2028, offering hands-on experience in building production software with mentorship.
Responsibilities
- Build and improve backend services and APIs, including REST, gRPC, and event-driven systems.
- Develop modular, reusable platform components and developer-facing tooling.
- Work with databases, queues, caches, and distributed systems.
- Improve latency, throughput, reliability, and cost through profiling, observability, and system design.
- Contribute to systems supporting AI inference, data pipelines, and customer-facing workflows.
- Partner with Product, ML, Infrastructure, and customer-facing teams to translate needs into technical solutions.
- Participate in code reviews, architecture discussions, and technical problem-solving.
- See your work deployed to real users.
Requirements
- Current undergraduate junior or senior graduating no later than June 2028.
- Pursuing a degree in Computer Science, Engineering, or a related technical field.
- Strong foundations in data structures, algorithms, object-oriented programming, systems, and databases.
- Experience building software in Go, TypeScript, Rust, Python, Java, C++, or a similar language.
- Experience building a backend service, API, data-intensive application, or developer tool.
- Understanding of API design, databases, distributed systems, and reliability concepts.
- Comfortable with Git, testing, and debugging.
- Interest in cloud or containerized deployment.
- Ability to break down ambiguous problems from first principles and communicate clearly.
- Curiosity, proactivity, and willingness to own technical problems while seeking feedback.
Skills
- Backend services
- APIs
- REST
- gRPC
- Event-driven systems
- Platform capabilities
- Developer tooling
- Databases
- Queues
- Caches
- Distributed systems
- Consistency
- Retries
- Idempotency
- Failure modes
- Latency optimization
- Throughput optimization
- Reliability improvement
- Cost optimization
- Profiling
- Observability
- System design
- AI inference
- Data pipelines
- Customer-facing workflows
- Technical design
- Implementation
- Testing
- Deployment
- Maintenance
- Modular components
- Reusable components
- Git
- Debugging
- Cloud deployment
- Containerized deployment
Work Type
- Internship
Experience Level
- Intern
- Undergraduate Junior
- Undergraduate Senior
Education Level
- Bachelor's Degree in Computer Science, Engineering, or related technical field
Benefits
- 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)
About the Company
- Brain Co. builds AI-native operating systems for large, regulated institutions, powered by agents and a proprietary platform called Atlas.
- The company is entering its next phase of production deployments on a national scale across government, insurance, health, and financial services.
- Joining Brain Co. offers the chance to shape the company and a new category of applied AI, with projects shipping to production and creating measurable customer value.
- Brain Co. is an applied AI startup founded by Elad Gil and Jared Kushner, backed by prominent Silicon Valley investors and builders.
- They build AI platforms and applications for important institutions, delivering impact on complex, real-world problems.
- Past successes include automating construction permitting, optimizing supply chains, and streamlining hospital patient care.
- The company has raised $30M in Series A funding and built a team of 40+ AI experts.
- Interns contribute to real production systems deployed at global scale.