Research Intern RL & Post-Training Systems, Turbo (Fall 2026)

Together AI•San Francisco, CA

10d•$58 - $63

About The Position

The Turbo Research team investigates how to make post-training and reinforcement learning for large language models efficient, scalable, and reliable. Our work sits at the intersection of RL algorithms, inference systems, and large-scale experimentation, where the cost and structure of inference dominate overall training efficiency and shape what learning algorithms are practical. As a research intern, you will study RL and post-training methods whose performance and scalability are tightly coupled to inference behavior, co-designing algorithms and systems rather than treating them independently. Projects aim to unlock new regimes of experimentation—larger models, longer rollouts, and more complex evaluations—by rethinking how inference, scheduling, and training interact.

Requirements

Pursuing a PhD or MS in Computer Science, EE, or a related field (exceptional undergraduates considered)
Have research experience in one or more of: RL or post-training for large models (e.g., RLHF, RLAIF, GRPO, preference optimization); ML systems (inference engines, runtimes, distributed systems); Large-scale empirical ML research or evaluation
Are comfortable with empirical research by designing controlled experiments, while interpreting noisy results and drawing principled conclusions
Can work across abstraction layers
Strong Python skills for experimentation
Willingness to modify inference or training systems (experience with C++, CUDA, or similar is a plus)

Nice To Haves

Publications at leading ML and NLP conferences (such as NeurIPS, ICML, ICLR, ACL, or EMNLP)
Understanding of model optimization techniques and hardware acceleration approaches
Contributions to open-source machine learning projects

Responsibilities

Study RL and post-training methods whose performance and scalability are tightly coupled to inference behavior, co-designing algorithms and systems rather than treating them independently.
Unlock new regimes of experimentation—larger models, longer rollouts, and more complex evaluations—by rethinking how inference, scheduling, and training interact.