Senior​/AI Scientist; Polytope Bio

About Astera

Astera is a private foundation on a mission to steer science and technology toward a better 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 accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive progress.

We have committed a $3.5B endowment to that work and fit the form of our support to the function it serves: grants, investments, and in-house research programs. We accept meaningful risk in the research we back and expect some of it to fail because transformative ideas are rarely the ones already proven. Our projects, and the foundation itself, operate like high-velocity startups, with a high performance bar and competitive compensation to match.

We are searching for leaders who are compelled by the challenge of driving high-impact change in the way science is funded, conducted, and communicated. You can read more about our mission, vision, and programming /vision.

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.

• 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.

• 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.

• 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,…