Machine Learning Engineer, PhD Intern

Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions.

In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month.

Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale.

As a 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:

• Partner with senior engineers and data scientists to develop machine learning models that improve product features and 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

• Currently pursuing a Doctorate degree in Computer Science, Data Science, or a related field

• Experience in supervised and/or unsupervised machine learning, ML operations

• 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

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

• Exposure to research in Large Language Models or Reinforcement Learning is a strong plus.

• Publications at leading conferences such as SIGIR, KDD, WSDM, RecSys

• 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