Research Scientist; Intern

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.

• 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.

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.

• 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.