Handshake AI Research Intern, Summer 2026

About HandshakeHandshake 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 feelWork hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutionsJoin a team with leadership from Scale AI, Meta, xAI, Notion, Coinbase, and Palantir, among othersBuild a massive, fast-growing business with billions in revenueAbout the RoleHandshake 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 TackleLLM 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 CapabilitiesCurrent 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 CreditPrior 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).#LI-AG3