Machine Learning Infrastructure Engineer - #4694

Menlo Park, CA March 24, 2026 Full Time

Our mission is to detect cancer early, when it can be cured. We are working to change the trajectory of cancer mortality and bring stakeholders together to adopt innovative, safe, and effective technologies that can transform cancer care. We are a healthcare company, pioneering new technologies to advance early cancer detection. We have built a multi-disciplinary organization of scientists, engineers, and physicians and we are using the power of next-generation sequencing (NGS), population-scale clinical studies, and state-of-the-art computer science and data science to overcome one of medicine’s greatest challenges. GRAIL is headquartered in the bay area of California, with locations in Washington, D.C., North Carolina, and the United Kingdom. It is supported by leading global investors and pharmaceutical, technology, and healthcare companies. For more information, please visit grail.com

The expected, full-time, annual base pay scale for this position is $190k-$255k.

This role may be eligible for other forms of compensation, including an annual bonus and/or incentives, subject to the terms of the applicable plans and Company discretion. This range reflects a good-faith estimate of the range that the Company reasonably expects to pay for the position upon hire; the actual compensation offered may vary depending on factors such as the candidate’s qualifications. Employees in this role are also eligible for GRAIL’s comprehensive and competitive benefits package, offered in accordance with our applicable plans and policies. This package currently includes flexible time-off or vacation; a 401(k) retirement plan with employer match; medical, dental, and vision coverage; and carefully selected mindfulness programs.

GRAIL is an equal employment opportunity employer, and we are committed to building a workplace where every individual can thrive, contribute, and grow. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender, gender identity, sexual orientation, age, disability, status as a protected veteran, , or any other class or characteristic protected by applicable federal, state, and local laws. Additionally, GRAIL will consider for employment qualified applicants with arrest and conviction records in a manner consistent with applicable law and provide reasonable accommodations to qualified individuals with disabilities. Please contact us at [email protected] if you require an accommodation to apply for an open position.

GRAIL maintains a drug-free workplace. We welcome job-seekers from all backgrounds to join us!

Responsibilities

Partner with research teams to identify computational pain points or limitations in performing computational experiments and analyses.

Design, build, and evolve software which usefully extends research capabilities, including infrastructure for distributed ML training and evaluation on large controlled genomic datasets.

Develop tools and processes that ensure GxP-compliant testing, patchability, and inference reproducibility for classifiers that are promoted to production use.

Develop and maintain the research team’s software environment, including tools to assess the health, performance, and cost of the system.

These summarize the role’s primary responsibilities and are not an exhaustive list. They may change at the company’s discretion.

Required Qualifications

5+ years of experience developing software supporting machine learning, scientific computing, or large-scale data processing systems

Strong programming skills in Python and a systems-level language such as Golang (preferred), Java, C#, C++, etc.

Experience working with modern machine learning frameworks such as PyTorch or TensorFlow

Experience with Distributed Computing paradigms (Spark, Ray, Flink, Beam, etc.)

A commitment to high-quality professionally engineered software

Strong communication skills with the ability to help developers from a wide range of software development backgrounds

BS in Computer Science, Engineering, Bioinformatics, or a related field, or equivalent practical experience

Preferred Qualifications

Good understanding of container orchestration through Docker and cloud technologies.

Experience with scientific computing tools: NumPy, Jupyter, R Notebook, etc.

Experience with techniques used in modern AI (including LLM) training

Experience with whole genome sequencing, whole exome sequencing, bisulfite sequencing, and/or whole transcriptome sequencing data

Practical experience setting up continuous integration systems, along with expertise in at least one build tool (e.g. Bazel (preferred), Buck, Maven, Gradle)

Familiarity with AWS services, best practices, and security

Advanced degree (MS or PhD) in computer science, engineering, bioinformatics or a related discipline