Research Scientist - Reasoning Systems (Intern)

About Xterra

Xterra is a Khosla Ventures-backed company building AI agents that reason about complex scientific problems. We’re not a wrapper around existing models, we’re training our own foundation models on top of large-scale proprietary datasets. This is a rare intersection of frontier AI and real-world scientific impact.

Xterra is still in stealth mode. Please reach out to us for a full picture.

The Role

We're looking for research scientists and research interns who want to work at the intersection of research and engineering — developing new methods for teaching AI systems to reason, and building the infrastructure to train and evaluate them at scale. You'll own problems end-to-end, from idea through experimentation to production.

We're hiring across multiple levels (intern through senior/staff) and will calibrate scope and expectations to match your experience.

What You'll Work On

• Reasoning via reinforcement learning: Designing and training reasoning systems using RLHF,

RLAIF, and reward modeling approaches, applied to geological hypothesis generation and

evaluation.

• Process reward models and verifiers: Developing fine-grained supervision over intermediate

reasoning steps — not just final answers — so the system learns to reason well, not just get

lucky.

• Search and planning: Exploring chain-of-thought strategies, search-time compute (e.g., Monte

Carlo Tree Search), and other techniques that enable deeper, more deliberate reasoning over

geological evidence.

• Scalable oversight: Contributing to alignment and oversight research — figuring out how to

reliably supervise models on geological tasks where ground truth is expensive, delayed, or

ambiguous.

• Infrastructure and experimentation: Building robust training pipelines, running large-scale

experiments, and iterating quickly across the research-to-production lifecycle.

• Evaluation: Contributing to meaningful benchmarks and evaluation methods for geological reasoning capabilities.

What We're Looking For

• Strong fundamentals in machine learning, with hands-on experience training large models (LLMs preferred but not required).

• Demonstrated experience with reinforcement learning — ideally applied to language models, but strong RL backgrounds from other domains (robotics, game-playing, scientific discovery) are valued.

• Comfort working across the research-engineering spectrum: you can write a paper and you can debug a distributed training job.

• Familiarity with at least some of: reward modeling, RLHF/RLAIF pipelines, search and planning methods, or AI alignment techniques.

• Publication record is a plus but not a strict requirement — we care more about the quality of your thinking and what you've built.

For Interns

We welcome outstanding PhD and Masters students (and exceptional undergraduates) for research internships typically lasting 12–16 weeks. Interns work on the same problems as full-time researchers, embedded in a team and owning a meaningful project from day one. What we look for in intern candidates:

•

Currently pursuing a graduate degree (PhD or Masters) in machine learning, AI, or a related field - or an undergraduate with significant research experience.

• Coursework or research experience in reinforcement learning, NLP, or deep learning.

• A strong project portfolio or publications demonstrating independent research ability.

• Eagerness to tackle open-ended problems and ship real experiments on real data.

At More Senior Levels, We'd Also Expect

• A track record of identifying and driving high-impact research directions independently.

• Experience mentoring other researchers and influencing technical strategy.

• Deep expertise in one or more of the core technical areas listed above.