Computational Scientist – Enzyme Design

Location: Greater Boston Area 

Overview

A well-established biotechnology organization is seeking a Computational Scientist to support enzyme design and optimization efforts. This role focuses on developing and applying advanced computational and machine-learning approaches to guide protein engineering programs in a collaborative research environment.

Responsibilities

• Develop and improve predictive computational models for enzyme design using modern machine-learning and deep-learning approaches.

• Incorporate internal experimental and sequence datasets to enhance model performance and robustness.

• Apply structure- and sequence-based computational methods to optimize protein properties such as stability, solubility, and catalytic activity.

• Maintain and improve computational workflows and infrastructure supporting bioengineering research.

• Design protein variants using established in silico methodologies to support ongoing research programs.

• Partner closely with laboratory scientists to design, interpret, and iterate on experimental validation studies.

• Communicate technical results and insights to interdisciplinary teams through presentations and written documentation.

• Manage multiple projects in a fast-moving research environment while adapting to shifting priorities.

Qualifications

• Ph.D. in Bioengineering, Biochemistry, Chemical Engineering, Computer Science, Statistics, or a related discipline with a focus on computational or AI-driven methods.

• Hands-on experience with deep-learning frameworks such as PyTorch or TensorFlow.

• Strong programming skills in Python; experience with additional scientific computing languages is a plus.

• Demonstrated application of computational methods to protein or enzyme engineering challenges.

• Solid understanding of protein sequence and structural analysis.

• Excellent written and verbal communication skills.

• Experience in industrial or applied biotechnology settings preferred.

• Familiarity with advanced deep-learning architectures (e.g., transformer-based or generative models) is a plus.

• Knowledge of enzymology and catalytic mechanisms is beneficial.

• Ability to work independently and collaboratively while managing multiple concurrent projects in a dynamic research setting.

Function: Discovery Research / Computational Biology / Data & AI