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BBrain Co.

Machine Learning Engineer, Applied AI Co-Op Intern, Winter 2027

San Francisco, CAListed 1h ago

  • Hybrid
  • Internship
  • Winter 2027
  • BS

About this role

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/ML Engineer Co-op Intern , you’ll work alongside experienced engineers and researchers to help design, build, and deploy machine learning systems that solve high-impact problems in healthcare, government, and energy .

This internship is designed for current undergraduate students (juniors and seniors) who want hands-on experience applying modern AI techniques—especially large language models—to real-world challenges. You’ll gain exposure to the full ML lifecycle while receiving mentorship and ownership over meaningful projects.

What You’ll Do

- Assist in designing and deploying LLM-powered applications to automate complex, real-world workflows
- Build and improve data pipelines and support model training, evaluation, and optimization
- Work with structured and unstructured data (e.g., text, documents, logs)
- Help prepare models and systems for production deployment and monitoring
- Collaborate closely with senior engineers, AI researchers, and product teams
- Learn best practices through code reviews , design discussions, and hands-on mentorship
- Gain exposure to customer-facing and real-world constraints, including working with public-sector institutions

You Might Be a Good 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 experience programming in Python
- Have coursework or project experience in machine learning or AI
- Have exposure to or interest in large language models and GenAI applications (e.g., agents, RAG, reasoning workflows)
- Have used or are eager to learn frameworks such as PyTorch, JAX, or TensorFlow
- Have strong fundamentals in data structures and algorithms
Are curious, proactive, and excited to learn quickly in a fast-paced environment
- Enjoy owning problems end-to-end with guidance and support

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)

Pay not listed
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Location
San Francisco, CA
Work model
Hybrid
Role type
Internship
Season
Winter 2027
Degrees
BS
Family
Machine Learning Engineer
Listed
1h · Oct 14, 2026
B

Brain Co.

Series A · 51–200 employees

  • AI & Machine Learning
  • SaaS & Enterprise Software
  • Government & Public Sector

Brain Co. builds agent-native operating systems and AI solutions for complex, regulated institutions across government, health, insurance, finance, and enterprise markets. Its Atlas platform supports secure, model-flexible deployments and industry systems for operational work such as permitting, claims, authorization, reporting, and reconciliation.

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