ABOUT HANDSHAKE

Handshake is the career network for the AI economy. 20 million knowledge workers, 1,600 educational institutions, 1 million employers (including 100% of the Fortune 50), and every foundational AI lab trust Handshake to power career discovery, hiring, and upskilling, from freelance AI training gigs to first internships to full-time careers and beyond. This unique value is leading to unparalleled growth; in 2025, we tripled our ARR at scale.

Why join Handshake now:

• Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel

• Work hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutions

• Join a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among others

• Build a massive, fast-growing business with billions in revenue

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 Summer 2026 internship where your work can ship directly into our production stack and become a publishable research contribution. To start between May and June 2026.

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.

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., Py Torch, JAX, Deep Speed, 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).