Head of Engineering DIA R&D

Head Of Engineering Dia R&D

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.

As a Head of Engineering in Diagnostics (DIA) R&D, you'll lead the teams that build the engineering foundation beneath our science, and the development of your people will be your first responsibility. You'll help them shape their own career paths and open the opportunities that let them grow, remove the roadblocks in their way, and set the clear goals that good performance management depends on.

You'll lead software and service delivery with focus, clearing impediments, managing stakeholders well and building efficiencies into the way teams collaborate.

Description of the Area:

You'll architect and deliver the next generation of DIA R&D engineering capabilities, leading teams that bring together advanced AI and ML, laboratory informatics infrastructure and computational science. You'll develop engineers who can work through the unusual complexity that comes when biology, chemistry and technology meet, from LIMS and ELN systems to agentic AI and cloud-scale data platforms. And you'll drive technical excellence and pace while holding the rigour and compliance standards that Diagnostics R&D depends on.

The Opportunity:

In this role, you'll have the room to drive technical excellence and shape platforms built for what comes next. You'll:

• Develop future capabilities:
  Build the engineering strengths the function will need next, and coach your people through the long arc of their careers, including the systemic roadblocks that hold whole teams back.
• Shape strategy and vision:
  Co-create the long-term technical direction and strategy for the computational science and AI/ML platform portfolio.
• Set the technical standard:
  Define the platform architecture for AI/ML infrastructure (model deployment, MLOps, vector databases), scientific computing (HPC and cloud compute), laboratory informatics (LIMS and ELN integration) and the data platforms beneath them.
• Own delivery:
  Take accountability for how the portfolio performs in production, holding the line on quality, reliability and the performance SLAs people depend on.
• Manage complex dependencies:
  Direct technical priorities across a portfolio that spans AI solutions, bioinformatics pipelines, laboratory systems and data infrastructure, keeping the architecture coherent and free of duplication.
• Engage senior stakeholders:
  Build the relationships that make engineering possible here, with research leadership, IT infrastructure, Quality and Compliance, enterprise architecture and cloud platform teams, and align priorities and resources across them.
• Sharpen engineering practice:
  Improve how the teams build, from CI/CD pipelines for validated systems to infrastructure-as-code, automated testing and model deployment.
• Steward budget and resources:
  Manage the team's budget, including headcount, operating costs and vendor contracts, and make the trade-offs that put resources where they matter most.

Who you are:

We're looking for a leader who pairs technical depth with regulatory discipline, and who has already built and run engineering organisations rather than stepping up to lead one for the first time. You'll bring:

• Education:
  
  A bachelor's or advanced degree, typically in an engineering, IT or business discipline.
• Engineering leadership: 10+ years in software and platform engineering, including 5+ years leading cross-functional technical teams, with managers as well as engineers reporting to you.
• Regulated life sciences: 5+ years in pharmaceutical R&D, biotech, diagnostics or another regulated life sciences environment, with a working command of GxP, 21 CFR Part 11, GAMP 5 and risk-based validation.
• Production AI and ML: 3+ years leading AI/ML engineering teams, with systems you've taken to production at scale rather than pilots: LLM-based platforms, MLOps frameworks such as MLflow or Sage Maker, vector databases and RAG workflows among them.
• Scientific and laboratory informatics:
  Hands-on experience with laboratory informatics platforms such as Sapio, Benchling or Lab Vantage, instrument integration, scientific computing environments (HPC and GPU infrastructure) and data standards such as FAIR and CDISC.
• Architecture and Dev Ops:
  Deep expertise across cloud platforms (AWS, Azure or GCP), microservices, API design, containerisation (Docker and Kubernetes) and Git Ops, with the standing to lead serious modernisation work.
• Leadership in practice:
  Strong business sense and genuine executive presence. You lead through others, coaching as much as deciding; you're comfortable with ambiguity, and you can translate technical…