Senior Scientist, Machine Learning Research

A-Alpha Bio • Seattle, WA

4d • Remote

About The Position

A-Alpha Bio is seeking a Senior Scientist, Machine Learning Research to turn large-scale protein interaction data into insights and models that advance antibody and protein engineering. The role develops AI/ML methods that learn from millions of protein-protein interaction data points to predict and engineer protein binding. The Senior Scientist will provide technical leadership on challenging AI/ML projects, including de novo design of antibodies or minibinders and models for affinity prediction and developability optimization.

The role works with ML Scientists to design and execute computational experiments and, with wet-lab colleagues, in vitro experiments. It also supports long-term AI/ML research initiatives and communicates scientific results to the ML Research team, the broader company, external collaborators, and partners. This is a remote-eligible position for candidates residing in the United States, with expected travel to the Seattle headquarters once per quarter. The position reports to the Director, Machine Learning Research.

Requirements

• PhD in a quantitative or life sciences field (such as biology, chemistry, physics, or computer science) and 4+ years of professional experience in software development and AI/ML, including 2+ years of hands-on AI/ML experience using biological sequence and/or structure data; or a bachelor's degree in a quantitative or life sciences field and 12+ years of relevant experience, including 6+ years of professional software development and AI/ML experience; or an equivalent combination of education and experience.
• Strong experience with deep learning, including modern software frameworks such as PyTorch and JAX; model architectures and approaches such as VAEs, transformers, GNNs, diffusion, and flow matching; and common protein engineering tasks such as protein language models, inverse folding, and protein structure prediction.
• Strong proficiency with data science best practices, including data wrangling and transformation, model training and selection, cross-validation, and deployment at scale.
• Excellent verbal and written communication skills, including the ability to explain methods, findings, and limitations to computational and experimental audiences and to external collaborators and partners.

Nice To Haves

• Expertise with cloud computing, particularly Amazon Web Services (AWS).
• Proficiency with best practices for using large language models (LLMs) and agentic AI tools to assist with code generation and modeling.
• Experience analyzing large experimental datasets and making scientifically defensible decisions about data quality, statistical methods, and interpretation.
• Thorough understanding of biological, chemical, and physical concepts governing molecular properties and interactions.
• Familiarity with the state of the art in antibody engineering, protein engineering, synthetic biology, or other high-throughput biology environments, particularly the relationship between sequence, structure, and function.

Responsibilities

• Design and implement novel AI/ML approaches for predicting and engineering protein-protein interactions, using experimental measurements, protein sequences, and structural information to address drug discovery and optimization challenges.
• Design rigorous benchmarks and evaluations of model performance, uncertainty, and generalization, using appropriate baselines and guarding against data leakage to ensure scientifically defensible and reproducible conclusions.
• Deliver data packages, model executables, technical reports, and other work products at a quality level suitable for company and external collaborator use, in accordance with assigned timelines.
• Contribute to the shared company codebase and database; explain technical judgments and decisions; author and review merge requests; document methods; organize data; and help roll out new capabilities and data to the team.
• Apply scientific discipline and critical thinking to propose and analyze experiments, stay current with emerging technologies and trends, and develop computational approaches that increase the value of AlphaSeq data for protein engineering.
• Provide technical leadership on assigned projects and interface across departments.
• Mentor colleagues and help write and publish technical blog articles or scientific papers.
• Represent A-Alpha Bio publicly at conferences and in scientific discussions with key collaborators.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree