Machine Learning Scientist II/Senior Scientist, Protein Design

Skyhook • Cambridge, MA

25d

About The Position

We are seeking a collaborative and motivated Machine Learning Scientist II or Senior Scientist with a passion for working at the intersection of human and synthetic biology. In this role, you will develop deep-learning methods and models to design functional proteins and analyze large proprietary data sets from high-throughput functional assays. As part of a growing multidisciplinary team, you will have the opportunity to take part in innovative research and development in a dynamic startup environment.

Requirements

• PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, Bioinformatics, Statistics, or related field with a minimum of 2+ years industry experience in pharma or biotech R&D required.
• Titling will be commensurate with experience
• Strong foundation in machine learning, including model development, training, evaluation, and application to large-scale biological datasets
• Experience with exploratory data analysis, statistical analysis, and data visualization to interpret experimental results and guide modeling decisions
• Proficiency in modern ML frameworks such as PyTorch or TensorFlow, and writing efficient, well-structured, and maintainable code (Python required)
• Experience working with cloud-based ML workflows and data pipelines (AWS preferred)
• Strong analytical and problem-solving skills; comfortable working in a dynamic, collaborative startup environment
• Clear written and verbal communication skills, with the ability to explain technical concepts to diverse audiences

Nice To Haves

• Experience with protein-focused ML models and tools (e.g., ESM, AlphaFold, or related frameworks)
• Experience designing or improving ML infrastructure and data pipelines, including cloud-based training, inference, data versioning, automation, and deployment
• Experience with NGS data analysis pipelines and integrating experimental data with computational workflows

Responsibilities

• Develop and advance deep-learning methods to design and optimize proteins
• Train and evaluate machine learning models using proprietary high-throughput assay data and public protein sequence, structure, and function datasets
• Design and deploy active learning strategies to accelerate protein design cycles
• Analyze large-scale experimental datasets to extract insights and inform protein engineering strategy
• Collaborate closely with experimental and computational scientists to integrate laboratory and in silico workflows, including NGS-based analyses
• Communicate computational strategies, results, and recommendations to multidisciplinary teams and leadership

What This Job Offers

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