Senior Scientist, Machine Learning Research
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 focuses on developing AI/ML methods that learn from millions of protein-protein interaction data points to predict and engineer protein binding. The Senior Scientist will take a technical lead on challenging AI/ML projects within the ML Research team, including de novo design of antibodies or minibinders and models for affinity prediction and developability optimization. The role involves working with other ML Scientists to design and execute experiments, both in silico and in vitro with wet-lab colleagues; supporting long-term AI/ML research initiatives; and communicating scientific results within the company and to external collaborators and partners. A-Alpha Bio is an early-stage biotechnology platform company building the foundational data layer for AI-driven protein engineering. It provides biopharma companies and model builders with ground-truth experimental datasets, including affinity and structure, for training, benchmarking, and validating AI protein models. The company also applies its data and models with select partners to engineer therapeutics and discover and optimize biologics. The team works in a fast-paced, highly dynamic, and interdisciplinary environment.
Requirements
- PhD in a quantitative or life sciences field and 4+ years of professional software development and AI/ML experience, including 2+ years of hands-on AI/ML experience using biological sequence and/or structure data; alternatively, a bachelor's degree in a quantitative or life sciences field with 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 deep learning experience, including modern software frameworks such as PyTorch or JAX; model architectures and approaches such as VAEs, transformers, GNNs, diffusion, and flow matching; and protein engineering tasks such as protein language models, inverse folding, and protein structure prediction.
- Strong proficiency in 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 explaining methods, findings, and limitations to computational and experimental audiences and external collaborators and partners.
Nice To Haves
- Expertise with cloud computing, particularly Amazon Web Services (AWS).
- Proficiency in 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
- Develop and implement novel AI/ML approaches to predict and engineer protein-protein interactions using experimental measurements, protein sequences, and structural information, and apply them to drug discovery and optimization.
- Design benchmarks and evaluations to assess model performance, uncertainty, and generalization, using appropriate baselines and preventing data leakage so conclusions are scientifically defensible and reproducible.
- Generate deliverables, such as data packages, model executables, and technical reports, to a quality level suitable for internal and external use and in accordance with assigned timelines.
- Contribute to the shared company codebase and database by authoring and reviewing merge requests, documenting methods, organizing data, explaining technical decisions, and helping roll out new capabilities and data.
- Propose and analyze experiments, keep 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 that interface across departments and mentor colleagues.
- Help write and publish technical blog articles or scientific papers, and represent A-Alpha Bio at conferences or scientific discussions with collaborators.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
