Senior/Staff AI Scientist (Polytope Bio)

Astera is a private foundation on a mission to steer science and technology toward an abundant future. We believe the coming years will bring an era of unprecedented scientific and technological advancement as exponential progress in AI converges with central advances in other fields to dramatically accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive scientific progress.

Unlike traditional non-profit research organizations, projects supported by Astera operate like high-velocity startups, allowing us to focus on ambitious goals, match structure to problem, and attract strong technical talent and leadership. You can read more about our mission, vision, and programming here.

Today's frontier biological AI models are trained almost entirely on static, pre-existing data. They are powerful pattern matchers, but lack feedback from real biology.

Polytope Bio is a residency project at Astera that is building the missing piece: a post-training engine that closes the loop between frontier AI models and high-throughput biology. Our 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 looking for a Senior/Staff AI Research Scientist to join our foundational team. You will be joining at the point of maximum leverage: early enough to influence the modeling approaches, experimental design, and training strategy. The project is resourced with significant compute, financial runway, and the ability to generate large-scale prospective biological datasets. The researcher in this role will work hands-on to develop and publish new reinforcement learning methods and generative AI models leveraging datasets created by our unique high-throughput biology platform.

While our immediate focus is on hands-on research and rapid execution, there is potential for the right candidate to evolve into a co-founding technical or leadership role in the event of a future spinout.

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  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.

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  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.

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  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.

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  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.

• 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.

• Familiarity with biological research (protein modeling, sequence models, structural biology, or adjacent areas).

• Experience building and scaling training infrastructure on large GPU clusters.

Preference for candidates able to co-locate in NYC or SF Bay Area. Remote work is possible for the right candidate.