Machine Learning Engineer, AI Enablement

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections,  where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.

The Position

At Roche, we advance science so that we all have more time with the people we love. A healthier future. For a world where everyone has access to the healthcare they need today and for the generations to come. For this, we believe the next breakthrough in medicine will be powered by the intersection of computational engineering and biology. We are building the infrastructure that transforms AI from an experimental concept into a reliable, high-impact utility for our researchers.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and AI to assist scientists to deliver more innovative and transformative medicines for patients worldwide.

Within the CoE, the Data and Digital Catalyst (DDC) organisation drives the modernization of computational and data ecosystems and integration of digital technologies across Research & Early Development. The AI Enablement Engineering group sits at the heart of this mission. We aren't just training models; we are building scalable systems, data pipelines, and intelligent applications that enable our scientists to work smarter, not harder. From optimizing model performance to designing end-to-end agentic workflows, our goal is to put powerful AI tools directly into the hands of those driving drug discovery to power data-driven science and accelerate decision making.

We are a cross-functional, impact-driven team that values cognitive diversity and technical excellence. Here, you’ll partner with biologists, computational scientists, data experts, and researchers to architect a future where AI/ML is an essential component of every scientific discovery. If you’re an engineer who thrives on solving complex problems, spans the stack with curiosity, and wants to build tools that truly change lives, we want to hear from you.

The Opportunity:

As a Machine Learning Engineer in AI Enablement, you will implement the systems that unlock the potential of our scientific data, accelerating the discovery of life-changing treatments. You won't just build models; you will design the infrastructure that makes AI an everyday utility for researchers.

You will partner with a diverse ecosystem of computational scientists, ML experts, product leaders, and DevOps engineers to architect and implement robust, scalable, and maintainable backend systems and applications. You will have the autonomy to own end-to-end features (from initial model evaluation to production deployment) within a team that prioritizes technical rigor, mentorship, and collective growth. We are looking for engineers who view engineering excellence and inclusive, cross-functional collaboration as fundamental to delivering high-impact scientific solutions.

Translate scientific and business needs into actionable technical design and hands-on implementation of agentic AI solutions. For this, you will be writing, testing, and shipping high-quality, maintainable production code and building scalable systems and data pipelines on a daily basis.

Contribute independently to defined projects by writing maintainable code and applying software engineering best practices (testing, CI/CD, documentation) alongside AI tooling and quantitative metrics to evaluate and scale ML systems.

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:

You are a practitioner of technical rigor: You hold a Master’s degree with 2+ years of professional experience or a PhD in Computer Science or a similar technical field. You value engineering best practices, such as testing, clean code, and documentation, as the foundation for scalable, high-impact systems.

You span the stack: You are comfortable working across the ML lifecycle, from GPU optimization and model performance to backend systems and cloud-native architecture (e.g., Kubernetes, AWS) with solid knowledge of statistics and ML theory as well as AI/ML frameworks, libraries, and toolsets

You value collaborative engineering: You view code reviews and architectural decisions as spaces for shared growth and teaching, not just validation. You excel at translating complex technical challenges into shared goals with diverse partners, from research scientists to DevOps engineers.

You are a continuous learner: You approach new frameworks and languages with curiosity. You are as comfortable mentoring others as you are learning from them, and you actively contribute to a culture of collective growth.

You are an excellent communicator: You excel at translating technical complexity into clear, actionable insights for diverse audiences. You communicate with empathy and precision, ensuring that technical trade-offs are well-understood and that all stakeholders, from research scientists to engineers, feel aligned and heard.

Preferred But Not Required:

Experience with biological data (ideally multimodal) 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.

Kindly note that this role requires working onsite approximately 3 days a week and does not offer relocation assistance.

 

 

Who we are

A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.

Let’s build a healthier future, together.

Roche is an Equal Opportunity Employer.