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 at astera.org/vision.

About Prism:

Prism is a program within Radial, the basic life sciences division of the Astera Institute. We are making protein motion measurable, predictable, and actionable by building open infrastructure for studying protein dynamics directly from experimental data. Function emerges from the ensemble of conformations a protein adopts and how it moves between them, and we are rebuilding the structural biology stack around that premise: data collection, modeling, representation, and interpretation. We work at the interface of experimental structural biology (crystallography, cryo-EM), machine learning, computational biophysics, and open scientific tooling. Our team spans computational biologists, ML researchers, software engineers, and program staff, collaborating across Radial, Astera, and partner institutions.

Key Responsibilities:

• Build ensemble-aware protein representations that integrate PLM and LLM embeddings with experimentally derived structural heterogeneity for functional prediction

• Design, develop, and maintain large-scale bioinformatic pipelines capable of processing and managing complex, high-dimensional datasets

• Fine-tune or architect ML models to capture sequence-structure-function relationships, with a focus on dynamic and conformational features

• Synthesize diverse data sources spanning evolutionary history, binding affinity, allostery, and functional annotations to improve model performance and biological relevance

• Collaborate closely with experimental partners to ground computational representations in real biological measurements and ensure models are continuously refined against experimental ground truth

• Contribute to the broader Prism infrastructure, helping establish community-wide standards and tools for dynamic structural biology

Required Skills and Qualifications:

• PhD in bioinformatics, computational biology, machine learning, or a related field.

• Strong understanding of protein structure and function.

• Demonstrated experience building large bioinformatic pipelines and managing high-dimensional datasets.

• Proficiency in fine-tuning or modifying ML models (e.g., transformer-based architectures).

• Familiarity with protein language models (ESM, AlphaFold, etc.) is a plus.

• Collaborative, team-oriented mindset with the ability to drive research questions from conception to execution.

Compensation

Commensurate with experience.

• Benefits summary