GRAIL is seeking a Staff Machine Learning Infrastructure Engineer for the Research Platform Engineering team. This is a software engineering role, charged with building and supporting systems executing machine learning and other analysis workflows on controlled data. You will empower computational biologists, data scientists, and statisticians in their quest to develop and refine powerful diagnostic products, by enabling efficient and flexible exploratory research and classifier development, and smoothing the productionization of their work.
The ideal candidate will bring a passion for reliable software infrastructure, distributed computing, reproducible research, and general problem-solving. Due to the highly connected nature of this position, the candidate should be a strong communicator with experience working with multidisciplinary teams.
This is a hybrid role based in Menlo Park, CA (moving to Sunnyvale, CA in Fall 2026). Our current hybrid policy requires on-site presence at least 40% of the time, including key in-person collaboration days. At our Menlo Park campus, Tuesdays and Thursdays are the key days where we encourage on-site presence to engage in events and on-site activities.
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
The expected, full-time, annual base pay scale for this position is $190k-$255k.
Top Skills
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