ABOUT EIT:

The Ellison Institute of Technology (EIT) Oxford’s purpose is to have a global impact by fundamentally reimagining the way science and technology translate into end-to-end solutions and delivering these solutions in programmes and platforms that respond to humanity’s most challenging problems.
EIT Oxford will ensure scientific discoveries and pioneering science are turned into products for the benefit of society that can have high-impact worldwide and, over time, be commercialised to ensure long-term sustainability.
Led by a faculty of world experts, EIT Oxford seeks to solve the world’s most challenging problems across four high-risk, high-reward, high-impact humane endeavours: health and medical science; food security and sustainable agriculture; climate change and clean energy; and government innovation in an era of artificial intelligence.
EIT Oxford is investing significant resources in a new world-class research and development facility in the Oxford Science Park. Set for completion in 2027, the state-of-the-art campus includes 300,000 sq ft of research laboratories, an oncology and preventative care clinic, and educational and meeting spaces. Together, they create the perfect environment for EIT Oxford experts to take ground-breaking ideas from research to broad implementation. The new facility will further EIT’s current partnership with the University of Oxford and become the new home for Ellison Scholars.
EIT Oxford is committed to cultivating a community where excellence is achieved through collaboration, trust, innovation and tenacity. We foster an environment where everyone’s experience and expertise are valued. We are curious and resilient in our efforts to drive long-term, sustainable innovation to meet humanity’s most enduring challenges.

REQUIREMENTS

As a founding leader of the AI & Data team, you’ll have the rare opportunity to shape the future of ML infrastructure at scale. We’re looking for a visionary VP of MLOps to grow and lead a team of top-tier engineers, ensuring seamless integration of cutting-edge ML models into production. Your work will directly fuel the Institute’s most ambitious research and development initiatives, driving real-world impact. As a key member of our leadership team, you’ll have the freedom to innovate, architect at scale, and build the foundational AI infrastructure that will power the next generation of discoveries. If you thrive in fast-paced, high-impact environments and want to be at the forefront of AI innovation, we’d love to have you on this journey!

EDUCATION & THOUGHT LEADERSHIP:

• Master’s or PhD in Computer Science, Data Science, or a related field, ideally
• Ability to represent EIT in key discussions on ML infrastructure, shaping the direction of the industry.
• Passionate about leveraging AI/ML to solve global challenges, aligned with EIT’s mission and values.

• Team Leadership & Strategy: Lead a multidisciplinary team of infrastructure and tooling professionals to develop and scale ML platforms that support hundreds—growing to thousands—of researchers and data scientists across various program areas.
• MLOps Platform Architecture: Oversee the design, implementation, and continuous improvement of scalable ML infrastructure, including cloud services, data storage and residency, networking, high-performance computing, and tooling frameworks.
• Multi-Environment ML Infrastructure: Deploy and manage ML workflows across heterogeneous environments, including cloud, on-premises, and hybrid setups, ensuring seamless integration and operational efficiency.
• High-Performance Computing & Experiment Logging: Optimize and manage large GPU clusters to support efficient training of foundation models and large-scale experimentation.
• System Integration: Collaborate with IT and engineering teams to ensure seamless integration of ML infrastructure with existing enterprise systems and workflows.
• Model Lifecycle Management: Develop robust systems and processes to manage the end-to-end ML model lifecycle—from research and development to deployment and monitoring in production environments.
• Model Optimisation: Apply deep knowledge of underlying infrastructure and compute systems to optimize ML models for performance, efficiency, and scalability.
• Product deployment: Bridge the gap between research and production by operationalizing experimental models into stable, user-facing production-grade services. Ensure deployments are reproducible, performant, and aligned with evolving product goals.
• Collaboration with Researchers & Data Scientists: Partner with research teams to translate cutting-edge ML models into production-ready solutions, ensuring alignment with business and technical requirements.
• Observability & Monitoring: Establish comprehensive logging, monitoring, and analytics systems to track model performance, detect anomalies, and ensure operational reliability in real-time.
• Compliance & Security: Ensure ML operations comply with relevant regulatory standards (e.g., GDPR, HIPAA) and industry best practices while implementing robust governance frameworks.
• Security & Risk Management: Design and enforce security protocols to protect ML infrastructure, data, and models from unauthorized access, cyber threats, and breaches, ensuring a defence-in-depth approach across the entire cloud and network stack.