Machine Learning Engineer (AI Enablement)

Roche; South San Francisco (CA); On-site; USD 148,000 - 274,000; Full time

Benefits offered by this job

Onsite presence at South San Francisco

Discretionary annual bonus

Equal opportunity employer

Job summary

Roche is seeking a machine learning engineer for its AI Enablement group in the Data and Digital Catalyst organization. You will build scalable AI/ML systems that empower scientists to accelerate drug discovery, working with computational scientists, AI/ML experts, and product leaders.

The role requires a bachelor’s degree and 1–3 years of AI/ML experience, with strong statistics knowledge, GPU optimization, and cloud-native skills. Onsite presence at South San Francisco campus is expected.

Qualifications

• Bachelor’s degree with 1–3 years AI/ML experience.
• Strong knowledge of statistics and ML theory.
• Experience with ML performance optimization and GPUs.
• Familiar with Kubernetes, databases, data lakes, and cloud-native AWS.
• Proven engineering best practices and collaborative work.
• Excellent communication and quick-learning mindset.

Responsibilities

• Design, develop, and test scalable AI/ML web apps and backend systems.
• Build tools to evaluate AI/ML model performance and quality.
• Collaborate with product managers and scientists to translate needs into specs.
• Develop and maintain systems for collecting and storing diverse scientific data.
• Implement or evaluate new AI/ML algorithms and techniques.
• Contribute to architecture decisions, code reviews, and process evolution.
• Be willing to span the stack and adapt beyond core area.

Skills

AI/ML frameworks

Statistics & ML theory

GPU optimization

Kubernetes

Databases & data lakes

Cloud-native architectures

Engineering best practices

Communication skills

Learning agility

Education

Bachelor's degree in a relevant field

Tools

AWS

Job description

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:

• Bachelor’s degree with 1-3 years of experience 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.

Benefits

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.