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 organisation, 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 optimise 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:

• Master’s with 3-5 years or Bachelors with 4-7 years experience and a degree in Computer Science or similar technical field, or equivalent experience in machine learning engineering roles.

• 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 But Not Required:

• Experience with imaging or biological data and processes is a strong plus.

• Experience working with scientists or in a research environment is advantageous.

• Experience with workflow automation, GenAI, and/or agents is a plus.

Onsite presence on our South San Francisco campus is expected for at least 3 days a week.

Relocation benefits are available for this job posting.

The expected salary range for this position based on the primary location of California is $147,500 - $273,900 of hiring range.  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 at the link provided below.

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