Scientist II, Applied Machine Learning

A-Alpha Bio • Seattle, WA

14h • $139,000 - $175,000 • Remote

About The Position

A-Alpha Bio is seeking a Scientist II to join its Applied Machine Learning team and turn large-scale protein interaction data into insights and models that advance antibody and protein engineering. The role leads computational work for internal research and external collaborations, from defining scientific questions and analysis plans through model development and communication of results. Using AlphaSeq datasets, the Scientist connects experimental measurements with protein sequence and structural information to understand binding, evaluate computational design methods, and guide future experiments. Success requires stating assumptions, defining goals and objectives, and explaining procedures thoughtfully.

The Scientist will work in a fast-paced, highly dynamic, and interdisciplinary environment. A-Alpha Bio is an early-stage biotechnology platform company building a foundational data layer for AI-driven protein engineering. It provides biopharma companies and model builders with ground-truth experimental datasets, including affinity and structure, and applies data and models with selected partners to engineer therapeutics and discover and optimize biologics. The role is remote-eligible for candidates residing in the United States, with expected travel to the Seattle headquarters once per quarter.

Requirements

• PhD in a relevant field with at least 2 years of experience working on large-scale data analysis using biological sequence or structure data; or a bachelor’s degree with at least 10 years of relevant experience, including at least 5 years working on large-scale data analysis using biological sequence or structure data; or equivalent education and experience.
• Demonstrated expertise in Python for scientific computing, data analysis, visualization, and machine-learning model development.
• Experience analyzing large experimental datasets and making scientifically defensible decisions about data quality, statistical methods, and interpretation.
• Strong understanding of model evaluation, including appropriate baselines, data leakage, and the limits of conclusions drawn from a benchmark.
• Ability to translate an open-ended scientific question into an executable plan and deliver reproducible results with limited supervision.
• Clear communication skills, including explaining methods, findings, and limitations to computational and experimental audiences.
• Experience contributing to a shared codebase through version control, code review, and documentation.

Nice To Haves

• Experience with antibody or protein engineering, particularly the relationship between sequence, structure, and binding.
• Familiarity with structural modeling of protein complexes and evaluation of structure prediction or generative design methods.
• Use of modern machine-learning frameworks such as PyTorch, TensorFlow, or Hugging Face.
• Experience processing and analyzing next-generation sequencing data, such as Illumina or Oxford Nanopore.
• Experience developing analytical tools or workflows used by other scientists.

Responsibilities

• Define and execute analysis plans for large-scale protein interaction datasets, including evaluating binding affinity, specificity, and the performance of antibody and protein design methods.
• Build, train, and apply machine-learning models using experimental measurements, protein sequences, and structural information.
• Design rigorous benchmarks and assess model performance, uncertainty, and generalization.
• Collaborate with experimental scientists to shape study designs, interpret results, investigate unexpected findings, and recommend follow-up experiments.
• Serve as computational lead for assigned projects and produce clear presentations, reports, and well-documented datasets for internal teams and external collaborators.
• Prepare work that may include open-source elements with attention to detail and narrative.
• Translate project-specific work into reliable analytical tools and workflows; contribute maintainable Python code, review contributions, and document methods for others.
• Identify and develop computational approaches that increase the value of AlphaSeq data for protein engineering.

What This Job Offers

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