Senior/Staff AI Scientist (Polytope Bio)

Astera Institute•San Francisco, NY

5d•Hybrid

About The Position

Polytope Bio is a residency project at Astera that is building a post-training engine to close the loop between frontier AI models and high-throughput biology. This work will power new applications in generative biology by aligning frontier AI models directly to experimental measurements of what actually folds, binds, and functions. We are seeking a Senior/Staff AI Research Scientist to join our foundational team. This role offers the opportunity to influence modeling approaches, experimental design, and training strategy. The project is well-resourced with significant compute, financial runway, and the ability to generate large-scale prospective biological datasets. The researcher will develop and publish new reinforcement learning methods and generative AI models using datasets from our unique high-throughput biology platform. There is potential for the right candidate to evolve into a co-founding technical or leadership role in a future spinout.

Requirements

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).
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.
Familiarity with modern reinforcement learning and preference-optimization methods for deep learning.
A 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 early-stage environment.
Comfort operating with ambiguity and a desire to build something new.
Excited to tackle hard problems and potentially transition into a technical co-founder in the future.

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.

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: 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 is measured in the lab and what the models learn are designed as a single, cohesive system.