Role at a glance
- Job function
-
AI & Data Machine Learning Engineering
- Salary
- Not yet verified Check the description for pay details; an amount or pay period has not been verified.
- Location
- South San Francisco, United States
- Work arrangement
- On-site
- Employment
- Full-time
- Experience
- Strong proficiency with AI/ML frameworks, libraries, and toolsets. Expert knowledge of statistics, machine learning theory, and algorithms. Strong knowledge of ML performance optimization, GPU best practices. Experience with kubernetes, relational databases, NoSQL databases, or data lakes, and experience working on cloud-native architectures in public clouds (ideally AWS). Proven understanding and application of engineering best practices. Excellent communication skills and ability to build trusted partnerships with internal and external collaborators. Ability to quickly acquire new technologies and programming languages and a passion for continuous learning. Preferred: Experience with biological data (ideally multimodal) and scientific processes. Experience working with scientists or in a research environment. Experience with workflow automation, GenAI, and/or agents.
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Role Summary
The Machine Learning Engineer in AI Enablement builds, owns, and improves scalable AI/ML systems that support scientific data use and research. The role works with scientists, computational specialists, product leaders, and other collaborators to develop AI/ML applications and infrastructure.
What You'll Do
- Design, develop, and test scalable AI/ML scientific web applications and backend systems.
- Build tools to evaluate model performance and develop ways to understand AI quality.
- Partner with product managers and scientists to understand user needs, shape requirements, and translate them into technical specifications.
- Develop and maintain systems for collecting, structuring, and storing scientific data for analytics and machine learning.
- Implement, adopt, or evaluate AI/ML algorithms and analytical techniques, and contribute to architecture decisions, code reviews, and...
Generated from the employer's posting. Verify important details before applying.
View full postingQualifications
Required: Strong proficiency with AI/ML frameworks, libraries, and toolsets; expert knowledge of statistics, machine learning theory, and algorithms; knowledge of ML performance optimization and GPU best practices; experience with Kubernetes, relational databases, NoSQL databases, or data lakes, and with cloud-native architectures in public clouds (ideally AWS); understanding and application of engineering best practices; excellent communication and partnership skills; and ability to quickly acquire new technologies and programming languages with a passion for continuous learning.
Preferred
- Experience with biological data (ideally multimodal) and scientific processes.
- Experience working with scientists or in a research environment.
- Experience with workflow automation, GenAI, and/or agents.
Original job description
Content provided by the employer
Original job description
Content provided by the employer
We advance science so that we all have more time with the people we love.
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
Within the CoE organization, the Data and Digital Catalyst (DDC) organisation drives the modernisation of our computational and data ecosystems and integration of digital technologies across Research and Early Development to enable our stakeholders, power data-driven science and accelerate decision-making.
The Engineering - AI Enablement group within DDC is accountable for… enabling AI! We do this across the board with our scientific and computational partners based on their goals. We help embed our AI strategy across our research organizations by providing employees with the tools and support needed to adopt AI into our daily work—helping us work smarter and enhancing our day-to-day work. We also build and deploy AI based solutions that reshape and transform business processes in order to unlock value at scale and optimize workflows. We also work on scaling up model training and inference, evaluating the quality of AI/ML models and output, and building impactful applications which accelerate the scientists doing the critical work of drug discovery and development. Partnering with colleagues to build, deploy and evolve a modern tech stack and utilities to enable our AI/ML and agentic efforts will be a key foundation to our success. Our aim is for everyone who can benefit from AI/ML to be able to leverage that utility where and when they need it, from data analysis to literature search to documentation writing. We are aiming for AI/ML to be an everyday utility. The team is cross-functional, impact driven, independent, and constantly evolving to meet the scientific needs.
The Opportunity:
As a machine learning engineer in AI Enablement, you will be working closely with folks that span the gamut from Computational Scientists, Research Scientists, AI/ML experts, Product leaders, DevOps, and everyone in between. You'll build, own, and constantly improve scalable AI/ML based systems that unlock the potential of our diverse scientific data, accelerating the discovery and development of life-changing treatments for patients.
- Design, develop, and test robust, scalable, and maintainable AI/ML facing scientific web applications and backend systems.
- Build tools to evaluate AI/ML model performance and establish new ways to understand AI quality.
- Partner with product managers and scientists to understand user needs, shape requirements, and translate them into actionable technical specifications.
- Develop and maintain systems for collecting, structuring, and storing diverse scientific data that support advanced analytics, machine learning, and other data-driven initiatives.
- Implement, adopt, or evaluate new AI/ML algorithms and analytical techniques
- Contribute to architectural decisions, code reviews, and the evolution of our development processes.
- Be willing to span the stack and contribute where needed, even outside of your core area of expertise.
- Stay up-to-date with emerging technologies and industry best practices and adopt a culture of continuous learning, collaboration, and curiosity.
Who you are:
- Strong proficiency with AI/ML frameworks, libraries, and toolsets.
- Expert knowledge of statistics, machine learning theory, and algorithms.
- Strong knowledge of ML performance optimization, GPU best practices.
- Experience with kubernetes, relational databases, NoSQL databases, or data lakes, and experience working on cloud-native architectures in public clouds (ideally AWS).
- Proven understanding and application of engineering best practices.
- Excellent communication skills and ability to build trusted partnerships with internal and external collaborators.
- Ability to quickly acquire new technologies and programming languages and a passion for continuous learning.
Preferred:
- Experience with biological data (ideally multimodal) and scientific processes.
- Experience working with scientists or in a research environment.
- Experience with workflow automation, GenAI, and/or agents.
Onsite presence on our South San Francisco campus is expected for at least 3 days a week.
Relocation Assistance benefits are not available for this posting.
The expected salary range for this position based on the primary location of San Franscico, CA is $147,600 $274,000. This position is open to both SE5 and SE6 levels, with final leveling to be determined upon completion of the interview process. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance.
This position also qualifies for the benefits detailed here:
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
About the company
Genentech
Large Enterprise
Genentech, a member of the Roche Group, is a biotechnology company focused on discovering, developing, manufacturing, and commercializing innovative medicines for patients with serious medical conditions. Founded in 1976, Genentech has played a pioneering role in the biotech industry, particularly in the development of targeted therapies for cancer and other diseases. The company is committed to advancing science and addressing unmet medical needs through cutting-edge research and collaboration, making it a leader in the field of biotechnology. With a strong emphasis on sustainability and corporate responsibility, Genentech is dedicated to improving patient outcomes and enhancing healthcare globally.