Machine learning engineer

We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here. We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well.

• Leading technical scoping and architectural decisions for high-impact ML systems
• Designing and building production-grade ML software, tools, and scalable infrastructure
• Defining and implementing best practices and standards for deploying machine learning at scale across the business
• Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities
• Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies
• Mentoring and developing junior engineers, actively shaping our team's engineering culture and technical depth

• You understand the full ML lifecycle and have significant experience operationalising models built with frameworks like TensorFlow or PyTorch
• You bring deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems
• You have demonstrable hands-on experience with cloud platforms (e.g., AWS, Azure, GCP), including architecture, security, and infrastructure
• You've extensive experience working with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale
• You thrive in fast-paced, high-growth environments, demonstrating ownership and autonomy in driving projects to completion
• You communicate exceptionally well, confidently guiding both technical teams and senior, non-technical stakeholders

• We are open to conversations about part-time hours.