Handshake AI Research Intern, Winter 2025

About Handshake AI

Handshake is building the career network for the AI economy. Our three-sided marketplace connects 18 million students and alumni, 1,500+ academic institutions across the U.S. and Europe, and 1 million employers to power how the next generation explores careers, builds skills, and gets hired.

Handshake AI is a human data labeling business that leverages the scale of the largest early career network. We work directly with the world’s leading AI research labs to build a new generation of human data products. From PhDs in physics to undergrads fluent in LLMs, Handshake AI is the trusted partner for domain-specific data and evaluation at scale.

This is a unique opportunity to join a fast-growing team shaping the future of AI through better data, better tools, and better systems—for experts, by experts.

Now’s a great time to join Handshake. Here’s why:

• Leading the AI Career Revolution: Be part of the team redefining work in the AI economy for millions worldwide.

• Proven Market Demand: Deep employer partnerships across Fortune 500s and the world’s leading AI research labs.

• World-Class Team: Leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, just to name a few.

Capitalized & Scaling: $3.5B valuation from top investors including Kleiner Perkins, True Ventures, Notable Capital, and more.

About the Role

Handshake AI builds the data engines that power the next generation of large language models. Our research team works at the intersection of cutting-edge model post-training, rigorous evaluation, and data efficiency. Join us for a focused winter 2025 internship where your work can ship directly into our production stack and become a publishable research contribution. To start between December 1st and January 15th.

Projects You Could Tackle

• LLM Post-Training: Novel RLHF / GRPO pipelines, instruction-following refinements, reasoning-trace supervision.

• LLM Evaluation: New multilingual, long-horizon, or domain-specific benchmarks; automatic vs. human preference studies; robustness diagnostics.

• Data Efficiency: Active-learning loops, data value estimation, synthetic data generation, and low-resource fine-tuning strategies.

Each intern owns a scoped research project, mentored by a senior scientist, with the explicit goal of an archive-ready manuscript or top-tier conference submission by spring 2026.

Desired Capabilities

• Current PhD student in CS, ML, NLP, or related field.

• Publication track record at top venues (NeurIPS, ICML, ACL, EMNLP, ICLR, etc.).

• Hands-on experience training and experimenting with LLMs (e.g., PyTorch, JAX, DeepSpeed, distributed training stacks).

• Strong empirical rigor and a passion for open-ended AI questions.
  

Extra Credit

• Prior work on RLHF, evaluation tooling, or data selection methods.

• Contributions to open-source LLM frameworks.

• Public speaking or teaching experience (we often host internal reading groups).