Principal Deep Learning/AI Engineer, Bioinformatics

What if the work you did every day could impact the lives of people you know? Or all of humanity?

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.

Position Summary

The Principal Deep Learning/AI Engineer Bioinformatics is a senior hands-on technical expert responsible for inventing, implementing, and validating AI- and ML-driven bioinformatics methods that directly power Illumina’s sequencing and analysis products. This role is primarily technical in nature, with deep personal ownership of algorithm design, model development, benchmarking, and production readiness. The role partners with product and engineering to ensure AI methods are scientifically sound, performant at scale, and ready for real-world customer and/or clinical use, while providing technical direction and mentorship.

Key Responsibilities

• Hands-on algorithm and model development: Design, prototype, and implement AI/ML methods for genomics and multiomics (e.g., basecalling, variant calling, error modeling, QC, anomaly detection, methylation, single-cell, metagenomics, assembly, interpretation).
• Technical ownership of AI in products: Serve as the technical authority for AI features embedded in Illumina software and pipelines, from concept through production release.
• People leadership: Lead a focused team to create, enhance, and sustain ML and AI products and tools.
• Benchmarking and validation: Define gold-standard datasets, evaluation metrics, and statistical validation approaches; personally review and approve model performance and scientific claims.
• Production ML rigor: Work directly with engineers to ensure models are reproducible, versioned, monitored, and robust in production (training pipelines, inference efficiency, regression detection).
• Data-centric AI leadership: Drive data strategy for model performance, including curation, labeling, augmentation, and bias/edge-case analysis across instruments, assays, and populations.
• Technical design reviews: Lead architecture and design reviews for AI and bioinformatics components; set coding, testing, and documentation standards.
• AI adoption through technical excellence: Enable adoption by delivering models that measurably improve accuracy, speed, cost, and usability—rather than through programmatic or organizational mandates.
• Regulated and clinical readiness: Provide technical input and documentation for design controls, traceability, and validation where AI methods are used in regulated contexts.
• Mentorship and technical guidance: Mentor senior and junior scientists and engineers as a technical authority and reviewer.
• External technical engagement: Patent, present, and -on occasion- publish technical work; evaluate external methods, tools, and collaborations for technical fit and differentiation.

Required Qualifications

• Typically requires a minimum of 15 years of related experience with a Bachelor’s degree; or 12 years and a Master’s degree; or a PhD with 8 years of experience; or equivalent experience.
• PhD (strongly preferred) or MS with equivalent depth in Bioinformatics, Computational Biology, Computer Science, Statistics, or a related field.
• 10+ years of hands-on experience developing algorithms and ML models for biological data, with sustained personal technical contribution.
• Proven track record of shipping AI/ML-driven bioinformatics methods into production software used by external customers.
• Deep expertise in genomics data types and workflows (FASTQ/BAM/CRAM/VCF, reference genomes, annotations, pipelines).
• Strong applied ML background, including deep learning, probabilistic modeling, and rigorous model evaluation.
• Demonstrated ability to work close to the code and data, including reviewing implementations and results.

Preferred Qualifications

• Experience with sequencing-instrument–adjacent AI (basecalling, signal processing, error modeling, run QC).
• Experience operating in regulated or clinical software environments.
• Publications and/or patents in AI, ML, or bioinformatics with clear product relevance.
• Experience optimizing models for scale, latency, and cost in production environments.
• Hands on experience leveraging AI tools for development wo