Scientist - Computational Biophysics

About the role

You will design methods that integrate molecular motion data with protein language models and experimental inputs to advance how biology is modeled. Success depends on establishing dynamic structural biology as a core scientific foundation, alongside static structure resources.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Build ensemble-aware protein representations that combine language model embeddings with experimental structural data to support functional prediction, and define how these representations serve downstream users.

Fine-tune or architect machine learning models to capture sequence, structure, and function relationships, with an emphasis on dynamic and conformational features that reflect biological reality.

Requirements

Hold a PhD in bioinformatics, computational biology, machine learning, or a related field, as stated in the degree requirement.

Demonstrate a strong understanding of protein structure and function at the molecular and systems level.

Show proven experience building large bioinformatic pipelines and managing high-dimensional, complex datasets across multiple experiments.

Demonstrate proficiency in fine-tuning or modifying machine learning models, including transformer-based architectures, for biological applications.

Apply a collaborative, team-oriented mindset to drive research questions from initial conception through execution and validation with partners.

Practical notes

This role is based at Emeryville HQ and operates in a full-time capacity. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Work in this field centers on computational methods that connect protein dynamics to biological function using diverse data sources. Tools commonly include protein language models, transformer-based architectures, and large-scale data pipelines. Success requires integrating evolutionary, mutational, and binding information into interpretable models. The role involves close collaboration with experimental teams to validate predictions. Expect to contribute to community standards that support dynamic structural biology.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Astera is a private foundation on a mission to steer science and technology toward an abundant future for all. 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.