Sierra Research Intern at Sierra to own a research project improving AI agent performance. Responsibilities include running experiments, extending τ-bench, and collaborating with researchers and engineers.
Responsibilities
Own a research project from start to finish, from framing the question and running experiments to writing and submitting a paper.
Push agent evaluation forward, for example by extending τ-bench to new domains, modalities, or long-horizon tasks, or by building new ways to measure reliability and consistency.
Study how agents reason, use tools, and follow policies over multi-turn conversations, and find out where and why they fail.
Explore post-training methods such as supervised fine-tuning and reinforcement learning to improve agent behavior, and measure the gains rigorously.
Work with large-scale, proprietary datasets of text and audio interactions to find research questions grounded in real customer problems.
Work with researchers and engineers across Sierra, and where it makes sense, turn your findings into improvements in production.
What you’ll bring
Currently pursuing a PhD in computer science, machine learning, or a related field.
A research track record, such as first-author publications or submissions at venues like NeurIPS, ICML, ICLR, ACL, EMNLP, Interspeech or ICASSP.
Strong experimental skills: you design clean experiments, build careful baselines, and are honest about what the results do and do not show.
Fluency in Python and modern ML tooling (for example, PyTorch, and LLM inference and training stacks).
High agency and a builder’s mindset. You’ll work across the stack to get an experiment running.
Qualification
A research track record
Required
Currently pursuing a PhD in computer science, machine learning, or a related field
A research track record, such as first-author publications or submissions
