Senior Engineer - Machine Learning & AI Enablement

Description

The company is building the infrastructure that turns AI from an experimental idea into a dependable tool for its researchers, in Research and Early Development.

The AI Enablement Engineering group sits within the Computational Sciences Center of Excellence, part of the Data and Digital Catalyst organisation that modernizes computational and data ecosystems across Research and Early Development.

The group builds scalable systems, data pipelines and intelligent applications so scientists can work faster and make decisions with data, from model performance tuning to end-to-end agentic workflows.

You will work alongside biologists, computational scientists, data experts and researchers in a cross-functional team that values diverse thinking and technical depth.

Responsibilities

• Lead the design, architecture and hands-on build of agentic AI solutions and scalable data pipelines for complex scientific problems.
• Own end-to-end delivery of high-impact products and workstreams, including setting standards for testing, CI/CD, documentation and evaluation.
• Make foundational architecture decisions, guide best practices through code reviews and resolve technical bottlenecks early.
• Work across the stack when needed, including outside your main area of expertise.
• Keep up with new technologies and practices, and keep learning, collaborating and asking questions.
• Drive technical strategy with computational scientists, product leaders and engineering teams, and take ownership of critical AI/ML products.

Requirements

• Hold a Master's degree with 5+ years of professional experience, or a PhD with 3+ years, in computer science or a related field.
• Show a record of leading complex workstreams and setting architectural best practices.
• Work across the full ML lifecycle, from GPU optimization and model performance to backend systems and cloud-native architecture such as Kubernetes and AWS.
• Bring solid knowledge of statistics and ML theory, plus AI/ML frameworks, libraries and toolsets.
• Deliver AI/ML products, spot risks early and guide cross-functional teams, from researchers to DevOps engineers, toward robust and maintainable solutions.
• Learn new frameworks and languages with curiosity, mentor others and learn from them in return.
• Explain technical complexity clearly to diverse audiences, so trade-offs are understood and stakeholders from research scientists to engineers feel aligned and heard.
• Nice to have: experience with biological data (ideally multimodal) and biological processes.
• Nice to have: experience working with scientists or in a research environment.
• Nice to have: experience with workflow automation, GenAI and/or agents.

Conditions

Equal opportunity employer.