Handshake AI Research Intern, Summer 2026
Role Overview
The Handshake AI Research Intern will work on innovative projects related to large language models (LLMs) under the guidance of a senior scientist. This role is suited for current PhD students who will own a scoped research project aimed at producing publishable research contributions while collaborating within a fast-growing team focused on AI data solutions.
Perks & Benefits
This remote internship offers a unique opportunity to contribute to cutting-edge AI research while collaborating with a world-class team. Interns can expect to gain valuable experience with direct impact on production systems, and the role encourages personal growth through mentorship and the potential for publication in top-tier conferences.
Full Job Description
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., 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).
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