Computational Protein Engineer

Role overview

Help users apply computational protein engineering methods to their research and product development work. This hybrid role combines customer education and project support with product feedback, feature design, and some implementation, connecting practical user needs with new tools and research capabilities. The role is remote, with the USA preferred, and includes customer visits and conference representation.

Responsibilities

- Guide users on computational protein engineering practices and help them apply available tools to their projects. - Lead product demonstrations, onboarding, tutorials, and other training; create supporting case studies and documentation. - Build ongoing customer relationships, identify pain points, and work with teams to develop useful solutions. - Use the platform directly and turn that experience and customer feedback into product roadmap ideas and feature designs. - Help design and implement features, including user-facing components and front end or backend APIs. - Contribute to research activities and deployment of new features, stay current with relevant methods, and represent the product at customer visits and conferences.

Requirements

- Bachelor's degree plus industry or research experience, or an advanced degree, in a quantitative field with a protein engineering background; a PhD is preferred. - Broad familiarity with computational protein engineering, including protein language models, structure prediction, and sequence and structure design tools. - Programming experience in Python, R, or a similar language, and familiarity with bioinformatics methods such as homology search, sequence alignment, structure analysis, phylogenetics, PSSMs, and HMMs. - Familiarity with deep learning and generative modeling, including autoregressive or diffusion approaches. - Strong written, visual, and spoken communication skills, with an interest in teaching, learning, and collaboration.

Nice to have

- Previous customer-facing experience or experience implementing deep learning models from scratch. - Hands-on antibody or enzyme engineering experience in a high-throughput setting. - Front end or backend software development, product and visual design, or experience with LLMs and retrieval-augmented generation.

Benefits and work setup

- Full-time remote role; USA preferred. Customer visits and conference attendance are part of the work. - Competitive base salary, equity, and health and dental insurance.