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AI/Machine Learning Engineer Intern

Handshake

Handshake

Software Engineering, Data Science
San Francisco, CA, USA
USD 49-49 / hour
Posted on Feb 22, 2026

Location

San Francisco, CA

Employment Type

Full time

Location Type

On-site

Department

Engineering

Compensation

  • $49 per hour

For cash compensation, we set standard ranges for all U.S.-based roles based on function, level, and geographic location, benchmarked against similar stage growth companies. In order to be compliant with local legislation, as well as to provide greater transparency to candidates, we share salary ranges on all job postings regardless of desired hiring location. Final offer amounts are determined by multiple factors, including geographic location as well as candidate experience and expertise, and may vary from the amounts listed above.

About Handshake

Handshake is the career network for the AI economy. More than 20 million knowledge workers, 1,600 educational institutions, and 1 million employers — including 100% of the Fortune 50 — trust Handshake to power career discovery, hiring, and upskilling. From freelance AI training gigs to first internships to full-time careers and beyond, we connect talent with opportunity at every stage.

This unique position in the ecosystem is driving exceptional growth — in 2025, we tripled ARR at scale.

Why join Handshake now:

  • Shape how careers evolve in the AI economy at global scale, with visible real-world impact

  • Work directly with leading AI labs, Fortune 500 partners, and top educational institutions

  • Help build a rapidly scaling business on a path toward multi-billion-dollar revenue

The Role

As a AI/Machine Learning Engineering Intern, you will contribute to building intelligent product experiences that help students discover and secure opportunities. Your work will span search, recommendations, matching, and other discovery systems that power job exploration on Handshake.

You will gain hands-on experience developing, evaluating, and deploying machine learning models in a production environment, learning how large-scale ML systems are designed, optimized, and maintained.

This is a paid, full-time summer internship with two cohort options:

  • May 18 – August 7, 2026

  • June 15 – September 4, 2026

In this role, you will:

  • Partner with senior engineers and data scientists to develop machine learning models that improve user experience

  • Build Agentic pipelines/workflows to improve the Handshake student/employer user experience

  • Contribute to experimentation, model evaluation, and performance monitoring

  • Participate in technical discussions, brainstorming sessions, and team reviews

  • Document methodologies and findings to support knowledge sharing and long-term system improvements

You Have

Must Haves:

  • Are currently pursuing a degree in Computer Science, Data Science, or a related field

  • Have strong programming skills in Python and experience with ML frameworks such as PyTorch or TensorFlow

  • Have exposure to software engineering best practices (version control, testing, code reviews)

  • Have familiarity with data analysis techniques and experience with SQL

  • Have strong problem-solving skills and the ability to work in a collaborative team environment

  • Have strong communication skills and are able to explain technical concepts effectively

Bonus Points:

  • Experience with cloud platforms such as AWS, Google Cloud, or Azure

  • Experience with modern coding tools like Cursor/Claude code/Codex

  • Prior internship or project experience in applied machine learning in domains such as NLP, search and recommendation systems

We Offer

Handshake provides benefits that help you feel supported and thrive at work and in life.
(The below benefits apply to US-based interns.)

  • 💰 Competitive hourly compensation

  • 📚 Mentorship and hands-on learning from experienced ML engineers

  • 💻 5 days/week in-office experience

  • 🤝 Structured intern programming and team events

Explore our mission, values, and open roles at joinhandshake.com/careers.