Founding Research Scientist

Xterra AI • San Francisco, CA

10h

About The Position

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.

Requirements

• 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.

Nice To Haves

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

Responsibilities

• Designing and training systems using RLHF, RLAIF, and reward modeling approaches, applied to scientific hypothesis generation and evaluation.
• Developing fine-grained supervision over intermediate steps - not just final answers - so the system learns to reason well, not just get lucky.
• Contributing to alignment and oversight research - figuring out how to reliably supervise models on complex scientific tasks where ground truth is expensive, delayed, or ambiguous.
• Building robust training pipelines, running large- scale experiments, and iterating quickly across the research-to-production lifecycle.
• Contributing to meaningful benchmarks and evaluation methods for domain-specific reasoning.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed