Senior/Staff AI Scientist (Polytope Bio)

Responsibilities

• Drive core research: Work closely with the technical founder and team to develop and execute the scientific vision, develop cutting-edge modeling approaches, and iterate rapidly on new ideas.
• 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: You will work directly with the technical team 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 And Experience

• Research Experience: PhD in machine learning, computational biology, or a related field, with a minimum of 1-2 years of post-PhD research or industry experience (accomplished researchers without a PhD are also encouraged to apply).
• Model Training: You have trained models from scratch, not just fine-tuned or called APIs. You have owned real training runs, know where they break, and know how to debug them.
• Modern Algorithms: Hands-on experience with generative diffusion models and/or transformer architectures.
• Reinforcement Learning: Familiarity with modern reinforcement learning and preference-optimization methods for deep learning.
• Builder mindset: A track record of strong research via publications, open-source work, shipped models, or equivalent evidence that you drive results. You are highly self-directed but thrive in a tight-knit, collaborative early-stage environment.
• Entrepreneurial spirit: Comfort operating with ambiguity and a desire to build something new. You are excited to tackle hard problems and potentially transition into a technical co-founder in the future.
• Strong Pluses: Familiarity with biological research (protein modeling, sequence models, structural biology, or adjacent areas). Experience building and scaling training infrastructure on large GPU clusters.

Location Preference

Candidates able to co-locate in NYC or SF Bay Area are preferred, but remote work is possible for the right candidate.

What we offer

• Compensation: Base salary of $200,000 to $300,000 during the residency.
• Upside: The potential to evolve into a co-founding technical role in a future spinout, contingent on project success and mutual fit.
• Scientific Impact: Authorship of high-impact open-source datasets, methods, and models.
• Resources: Significant secured runway and dedicated GPU resources.
• Unique Environment: A rare combination of frontier ML work directly coupled to a purpose-built, high-throughput experimental engine.
• Comprehensive Benefits: Full benefits package including health insurance, a company sponsored retirement plan, vision, dental, and more.

Compensation Range

$200K - $300K