Scientist II, Applied Machine Learning

Location: Remote-eligible for candidates residing in the United States, with expected travel to the Seattle headquarters once per quarter.

Starting salary: $139,000–$175,000 annually.

Company

hirify.global is an early-stage biotechnology platform company building a foundational data layer for AI-driven protein engineering through large-scale experimental protein interaction datasets and models.

What you will do

• Define and execute analyses of large-scale protein-protein interaction datasets, including binding affinity, specificity, and antibody and protein design performance.
• Build, train, and apply machine learning models using experimental measurements, protein sequences, and structural information.
• Design rigorous benchmarks and evaluate model performance, uncertainty, generalization, baselines, data leakage, and limitations.
• Collaborate with experimental scientists to shape studies, interpret results, investigate unexpected findings, and recommend follow-up experiments.
• Lead computational projects for internal teams and external partners, producing presentations, reports, and well-documented datasets.
• Build reusable analytical tools and workflows, contribute maintainable Python code, review contributions, document methods, and develop new computational approaches.

Requirements

• PhD in a relevant field with 2+ years of experience in large-scale data analysis using biological sequence or structure data; or a bachelor’s degree with 10+ years of relevant experience, including 5+ years in large-scale analysis of biological sequence or structure data; or equivalent education and experience.
• 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 benchmark conclusions.
• Ability to translate open-ended scientific questions into executable plans and deliver reproducible results with limited supervision.
• Clear communication skills for explaining methods, findings, and limitations to computational and experimental audiences.
• Experience contributing to shared codebases through version control, code review, and documentation.

Nice to have

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

Culture & Benefits

• Fast-paced, highly dynamic, and interdisciplinary work environment.
• Work with a diverse, meritocratic, and supportive team focused on scientific impact and collaboration.
• Remote-eligible arrangement with required quarterly travel to the Seattle headquarters.
• Exempt position in a company developing and scaling the AlphaSeq high-throughput protein interaction platform.