Research Intern

Sierra’s Applied Research team works to make the models behind our agents better, faster, and cheaper across many kinds of tasks. Our work covers post-training open-weight models for specialized capabilities, new approaches to voice AI, and the benchmarks the field uses to measure agents, including τ-bench.

As an Applied Research Intern, you’ll join the team for a research project, working with a mentor, you’ll pick a problem that matters to how AI agents perform in the real world, run rigorous experiments, and potentially write up what you find for a top venue. If you want your research to reach both the research community and production agents used by millions of people, come do it at Sierra.

WHAT YOU’LL DO

- 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.

- Clear, direct communication, both in writing and when presenting research.

EVEN BETTER

- Research experience with LLM agents, tool use, or multi-turn dialogue.

- Experience designing benchmarks or evaluations, especially ones tied to real-world outcomes.

- Experience with supervised fine-tuning, reinforcement learning, or long-horizon optimization.

- Experience with speech, voice, or multimodal models.

- Open-source contributions to ML research code or benchmarks.

INTERN QUALIFICATIONS

- The internship is open to students currently enrolled in a PhD program.

- Duration and start dates are flexible. Interns are embedded with the Applied Research team and work under the guidance of a mentor.

- The internship is based in our San Francisco office. We are in person Monday through Thursday.