Senior Engineer - Machine Learning & AI Enablement

Lead technical design, architecture, and hands-on implementation of agentic AI solutions and scalable data pipelines to solve complex scientific challenges. Drive end-to-end delivery of high-impact products and workstreams, establishing software engineering best practices (testing, CI/CD, documentation) and evaluation standards. Shape foundational architecture decisions, champion best practices through code reviews, and proactively resolve technical bottlenecks. 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.

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.

• You hold a Master’s degree with 5+ years of professional experience or a PhD with 3+ years in Computer Science or a related field, with a track record of driving complex workstreams and establishing architectural best practices.

• 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 excel at delivering AI/ML products, proactively identifying risks, and guiding cross-functional teams (from researchers to DevOps engineers) toward robust, maintainable solutions.

• 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 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.

• 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.