Machine Learning Engineer, Applied AI Co-Op Intern, Winter 2027
San Francisco Bay Area · San Francisco, California, United States
- Pay
- Pay not listedSimilar roles state $165K–$240K
- Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Engineering › AI/ML Engineering — team source
Department: Engineering › AI/ML Engineering
From the employer’s posting
What you’ll work on
Full postingHelp design and deploy LLM-powered applications that automate real workflows
Build and improve data pipelines and support model training and evaluation
Work with structured and unstructured data such as text, documents and logs
Help prepare models and systems for production deployment and monitoring
From the employer’s posting
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
What you’ll bring
All qualificationsCore experience
- Experience programming in Python
- Coursework or project experience in machine learning or AI
- Strong fundamentals in data structures and algorithms
Qualification wording
Have experience programming in Python
Have coursework or project experience in machine learning or AI
Have strong fundamentals in data structures and algorithms
Tools in this posting
- Python
- PyTorch
- TensorFlow
Source — Tool mentions in context
- 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
About Brain
Rebuild how the world works, to make institutions work better for the people they serve.
In the employer’s words · Read in context · Company website ↗
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 16, 2026 · 14 days ago
- Recorded sightings
- 16
- Last seen by us
- Oct 29, 2026
- Employer says posted
- Oct 9, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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- Have your resume and examples of relevant work ready.
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Job description
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 NowBrain 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 environmentEnjoy 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)
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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