Senior Scientist, Machine Learning Research (AI)

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

Salary: USD 164,000–206,000 annually

Company

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

What you will do

• Develop AI/ML methods to predict and engineer protein-protein interactions using experimental measurements, protein sequences, and structural information.
• Design and evaluate models for antibody and minibinder de novo design, affinity prediction, and developability optimization.
• Build rigorous benchmarks and evaluations covering model performance, uncertainty, generalization, baselines, and data leakage.
• Deliver data packages, model executables, and technical reports suitable for internal use and external collaboration.
• Contribute to the shared codebase and database through implementation, reviews, documentation, data organization, and capability rollouts.
• Lead technical projects, mentor colleagues, collaborate across departments, and communicate results through papers, technical articles, conferences, and partner discussions.

Requirements

• PhD in a quantitative or life sciences field with 4+ years of professional software development and AI/ML experience, including 2+ years using biological sequence and/or structure data; alternatively, a bachelor's degree with 12+ years of relevant experience, including 6+ years in software development and AI/ML, or an equivalent combination.
• Strong experience with deep learning, PyTorch, JAX, variational autoencoders, transformers, graph neural networks, diffusion, flow matching, protein language models, inverse folding, and protein structure prediction.
• Strong proficiency in data science practices including data wrangling and transformation, model training and selection, cross-validation, and deployment at scale.
• Excellent verbal and written communication skills for explaining methods, findings, and limitations to computational, experimental, and external audiences.

Nice to have

• Expertise with cloud computing, particularly AWS.
• Experience with large language models and agentic AI tools for code generation and modeling.
• Experience analyzing large experimental datasets and making scientifically defensible decisions about data quality, statistical methods, and interpretation.
• Understanding of biology, chemistry, and physics governing molecular properties and interactions.
• Familiarity with antibody engineering, protein engineering, synthetic biology, or other high-throughput biology environments, especially the relationship between sequence, structure, and function.

Culture & Benefits

• Fast-paced, highly dynamic, and interdisciplinary environment.
• Supportive, diverse, meritocratic team focused on scientific impact and continuous improvement.
• Role is remote-eligible within the United States, with quarterly travel to the Seattle headquarters.
• Work is connected to AlphaSeq, a high-throughput platform for measuring protein-protein interactions at scale.