Principal AI Scientist

Responsibilities

• Co-direct the research program: Partner directly with the founder to shape the scientific vision, research direction, and technical execution.
• Develop RL feedback loop: Design, implement, and improve model post-training methods that translate high-throughput biological measurements into direct reward signals for biological language models.
• Hands-on engineering: Work directly with the technical founder to architect model training infrastructure and build, run, and debug models, training loops, and evaluation metrics.
• Bridge wet/dry lab: Partner with the experimental team to ensure that what we measure in the lab and what the models learn are designed as a single, cohesive system.

Qualifications

• PhD in machine learning, computational biology, or a related field with a minimum of 2 to 5 years of industry research experience post-PhD (accomplished researchers without a PhD are also encouraged to apply).
• Trained models from scratch, not just fine-tuned or called APIs. Owned real training runs, know where they break, and know how to debug them.
• Hands-on experience with generative diffusion models and/or transformer architectures.
• Deep familiarity with modern reinforcement learning and preference-optimization methods for deep learning.
• Track record of strong research via publications, open-source work, shipped models, or equivalent evidence that you drive results.
• Highly self-directed but thrive in a tight-knit, collaborative founding partnership.
• Comfort operating with ambiguity and research ownership. Excited to build the infrastructure and grow the team.

Nice-to-Haves

• Familiarity with biological research (protein modeling, sequence models, structural biology, or adjacent areas).
• Experience building and scaling training infrastructure on large GPU clusters.
• Past team management and technical leadership experience.