Machine Learning Scientist, Protein Design

About Skyhook Bio

Skyhook accelerates the discovery and development of differentiated therapeutics by accessing vast biological design spaces previously intractable with current state‑of‑the‑art technologies. Our differentiated platform employs efficient AI‑guided design, synthesis, and functional screening of diverse programmable biomolecule libraries at unprecedented speed and scale.

We are seeking an innovative, self‑motivated, and team‑oriented Machine Learning 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 a member of a growing and engaged team, you will take part in leading innovative research and development and collaborate with a highly skilled multidisciplinary team in a fast‑paced startup environment.

• Train and deploy deep neural network models using public and proprietary protein sequence, structure, and function data
• Establish automated processes to continuously evaluate and improve our protein design methodology
• Apply and develop deep‑learning models to learn from large experimental datasets
• Build and maintain database infrastructure for long‑term data storage and versioning on AWS
• Collaborate closely with experimental and computational scientists to develop integrated experimental/​in silico pipelines, including for NGS data analysis
• Develop efficient, high‑quality code and data pipelines
• Present computational approaches and results to colleagues in regular update meetings

• PhD in Computer Science, Machine Learning, Computational Biology, Biophysics, Bioinformatics, Statistics, or related field. 2+ years industry experience in pharma or biotech R&D preferred. Position leveling 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 Tensor Flow, and writing efficient, well‑structured, and maintainable code (Python required)
• Experience working with cloud‑based ML workflows and data pipelines
• 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

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

Raven is RA Capital’s healthcare incubator. From discovery to delivery, whether Raven incubates, accelerates or rejuvenates, Raven’s goal is to turn scientific breakthroughs into transformative therapies and get them quickly, safely and efficiently to the patients that need them most. Raven’s experienced scientists, operators, and healthcare innovators have deep sector expertise across therapeutics, diagnostics, devices and services. They have brought hundreds of therapeutics into development, managed hospital systems, optimized clinical trials and navigated payor and regulatory systems to deliver patient impact - and they are ready to work with you.

Founded in 2004, RA Capital Management is a multi‑stage investment manager dedicated to evidence‑based investing in public and private healthcare, life sciences, and planetary health companies. RA Capital creates and funds innovative companies, from private seed rounds to public follow‑on financings, allowing management teams to drive value creation from inception through commercialization and beyond. RA Capital’s knowledge engine is guided by our Tech Atlas internal research division, and Raven, RA Capital’s company‑building team, offers entrepreneurs and innovators a collaborative and comprehensive platform to explore the novel and the re‑imagined.

RA Capital has more than 150 employees and over $10 billion in assets under management.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.