Senior Bioinformatics Scientist – AI/ML for Protein Engineering

Responsibilities

• Develop and apply tailored bioinformatics tools and pipelines to design, engineer, and/or discover novel enzymes.
• Apply modern AI approaches, including protein language models, generative design methods, and structure-based machine learning, to predict and optimize enzyme function.
• Evaluate application, development, and implementation of novel computational methods and AI technologies to leverage protein sequence, structure, and/or experimental data for enzyme engineering.
• Create reusable templates, scripts, dashboards, or lightweight applications that allow project teams to run routine analyses and review computational recommendations more efficiently.
• Contribute to standards for data capture, model evaluation, design rationale, and reproducible computational workflows.
• Analyze high-throughput screening and sequencing datasets to guide protein optimization.
• Participate in multi-disciplinarily protein engineering projects and interact with engineers, chemists, biophysicists, and protein scientists, to deliver protein variants with desired properties.

Requirements

• Typically requires a minimum of 5 years of related experience with a Bachelor’s degree; or 3 years and a Master’s degree; or a PhD without experience; or equivalent work experience.
• Practical experience developing and applying AI/ML methods to protein and enzyme engineering.
• Solid programming skills in at least one of the major programming languages (e.g., C/C++, Python, R).
• In-depth knowledge of protein sequence or structure modelling, protein-ligand interaction predictions and/or molecular simulations.
• Strong fundamentals in statistical modelling, algorithm design, data analysis, and/or visualization methods.
• Excellent verbal and written communication skills.
• Collaborative, open and self-aware, team player, and able to rapidly integrate into cross functional teams.
• Highly motivated individual with proven ability in thinking innovatively and the proven track record of productive research and development.

Preferred Experience

• Experience with foundational AI/ML models and protein language models for protein engineering, including generative protein design, zero-shot variant scoring, supervised learning, model-guided library design, denoising and uncertainty-aware learning, and multi-parameter optimization of protein properties.
• Experience with structure prediction, protein-ligand or enzyme-substrate modeling, molecular dynamics, docking, or biophysical analysis (e.g. Rosetta, Protein MPNN, RFdiffusion, Boltz, Alphafold).
• Experience developing data standards, assay schemas, dashboards, or lightweight tools for experimental scientists.
• Experience with AI agents.
• Deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Large-scale biological data analysis using cloud or high-performance computing environments.
• Experience with high-performance computing, job schedulers, and/or GPU computing.
• Experience with experiments within protein engineering, e.g. directed-evolution, high-throughput screening.
• Knowledge of best software development practices and code version control, e.g. Git.
• Knowledge of UNIX environment and shell scripting.
• Broad knowledge of bioinformatics, genomics, and/or proteomics database, tools, and/or algorithms.
• Knowledge of biochemistry, enzymology, and/or molecular biology.
• Familiarity with next generation sequencing technologies, tools, and/or workflows.