- Strong software engineering experience building production backend systems, infrastructure, or distributed systems.
- Research or production experience in machine learning, or another quantitative discipline.
- Familiarity with LLM APIs, tool calling, agent runtimes, or workflow orchestration.
- Strong quantitative judgment and the ability to determine whether an apparent improvement is real, reproducible, and meaningful.
- Ability to move between research questions, data analysis, system design, and production implementation.
- Experience or strong interest in AI for science, scientific agents, computational research, or automated scientific discovery.
- Experience working in AI-native teams that use coding agents or automation extensively.
- High ownership, clear communication, and strong engineering judgment.
We care more about demonstrated ability than a particular credential. Relevant backgrounds may include ML or LLM engineering, academic research paired with substantial software development, scientific computing, or production systems engineering with demonstrated quantitative experience.