Senior Bioinformatics Scientist – AI/ML for Protein Engineering

Illumina•San Diego, CA

32d•$129,400 - $194,000•Onsite

About The Position

At Illumina, we are expanding access to genomic technology to realize health equity for billions of people around the world. Our efforts enable life-changing discoveries that are transforming human health through the early detection and diagnosis of diseases and new treatment options for patients. Working at Illumina means being part of something bigger than yourself. Every person, in every role, has the opportunity to make a difference. Surrounded by extraordinary people, inspiring leaders, and world changing projects, you will do more and become more than you ever thought possible. We are seeking a Senior Bioinformatics Scientist specializing in AI-enabled protein design and engineering to help invent the next generation of enzyme reagents that power Illumina sequencing platforms. Working at the intersection of machine learning, structural biology, and protein engineering, you will apply state-of-the-art computational approaches to design enzymes that expand the performance and capabilities of Illumina technologies. The role is based in San Diego. This is an exciting opportunity to be a part of Illumina’s continued growth.

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.

Nice To Haves

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.

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.