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.
Our team develops Next Generation Sequencing (NGS) solutions used by researchers and clinicians worldwide, providing sample-to-answer pipelines with high reliability, speed, and accuracy of results. We develop machine learning solutions across Illumina’s portfolio, from sequencing functions to analysis and interpretation algorithms. DRAGEN, our secondary analysis platform, has industry leading performance and is used for clinical and research work. We also develop algorithms for on-sequencer pipelines including super-resolution, basecalling, denoising. Advanced AI applications drive transformational genetic insights that improve understanding of human biology, cancer and rare disease.
We are seeking an ML Ops engineer to join our team. This role will develop, implement, and optimize data pipelines for ML systems across Illumina’s products, including DRAGEN and high-throughput sequencing systems like Novaseq X, the highest throughput sequencer in the industry. You will collaborate with cross-functional teams (ML, implementation, bioinformatics, optics and imaging, test) to store and process petabytes of highly heterogenous data (images, sequencing output, population data, truth sets, DNA, RNA, multi-omics, variant calls).

Responsibilities:

• You will create and maintain code, documentation, testing and deployment frameworks, tools and infrastructure, working closely with engineers, researchers and domain experts on AI/ML models and pipelines
• Work with experts across software engineering, hardware engineering, ML and data science, optics and imaging, precision motion, embedded systems, test
• Develop environments for building, testing, tracking production AI models and data across data pipelines used in primary and secondary genomic analysis
• Benchmark, track and document model performance, enable continuous improvement of pipelines
• Be a technical expert to help internal customers & teams to develop AI models within a consistent ML environment, automate training and data/model management
• Participate in setting the long-term roadmap for technical solutions using ML across multiple pipelines
• Stay up to date on best practices and drive adoption of standardized processes across the team and wider organization
• Standardize the management of ML models, operationalize ML pipelines; support release, activation, monitoring

All listed tasks and responsibilities are deemed as essential functions to this position; however, business conditions may require reasonable accommodations for additional tasks and responsibilities.

Qualifications:

• Bachelors or Masters in Computer Science or a related technical field, or equivalent experience
• 2+ years of relevant experience in machine learning and operations, ideally in a hands-on MLOps role (extraordinary applicants with less experience also considered)
• Experience deploying APIs and packages
• Experience with ML ops platforms (MLFlow, W&B etc; Kubernetes/Docker, dask, rapids.ai, Ray, similar)
• Experience with ML frameworks (Tensorflow, keras, Pytorch, xgboost, sklearn)
• Strong Python coding skills – experience with unit testing, code reviews, version control
• Strong background and interest in ML Ops, Dev Ops, Data Engineering
• Self-starter, good problem-solving skills, ability to push forward project objectives both through individual effort and team collaboration
• Experience with CI/CD platforms, ability to design a technical roadmap and influence/build alignment

All listed requirements are deemed as essential functions to this position; however, business conditions may require reasonable accommodations for additional tasks and responsibilities.

Additional Nice-to-Haves:

• Bioinformatics, ML, software engineering principles, software test, applied math background and/or experience
• NGS knowledge
• Distributed compute for big data - HPC, dask, Ray etc.
• Experience with AWS, GCP, Azure
• Visualization experience (plotly, matplotlib, etc)
• Familiarity with bioinformatics workflows primary and/or secondary analysis pipelines
• Experience with revision control (git, github Actions)
• Experience with ML acceleration technology (FPGA, GPU, etc)
• Strong Linux/Unix fundamentals
• Strong documentation and presentation skills
• Machine learning experience/knowledge

Degree and Job Experience Requirements:

• The candidate could have a degree from any of the following fields: Bioinformatics, Biology, Physics, Electrical Engineering, Computer Science, Software Engineering, Applied Math, related topics
• Bachelor’s, Master’s, or Ph.D.
• Job experience: the role can be morphed to accommodate candidates from recent graduates to experienced professionals

Illumina believes that everyone has the ability to make an impact, and we are proud to be an equal opportunity employer committed to providing employment opportunity regardless of sex, race, creed, color, gender, religion, marital status, domestic partner status, age, national origin or ancestry, physical or mental disability, medical condition, sexual orientation, pregnancy, military or veteran status, citizenship status, and genetic information

• You will create and maintain code, documentation, testing and deployment frameworks, tools and infrastructure, working closely with engineers, researchers and domain experts on AI/ML models and pipelines
• Work with experts across software engineering, hardware engineering, ML and data science, optics and imaging, precision motion, embedded systems, test
• Develop environments for building, testing, tracking production AI models and data across data pipelines used in primary and secondary genomic analysis
• Benchmark, track and document model performance, enable continuous improvement of pipelines
• Be a technical expert to help internal customers & teams to develop AI models within a consistent ML environment, automate training and data/model management
• Participate in setting the long-term roadmap for technical solutions using ML across multiple pipelines
• Stay up to date on best practices and drive adoption of standardized processes across the team and wider organization
• Standardize the management of ML models, operationalize ML pipelines; support release, activation, monitorin