About this role
This Machine Learning Engineer role at Faculty in London suits professionals with strong Python skills, cloud infrastructure experience, and a background in operationalising models across various sectors. The position involves delivering bespoke AI solutions, defining scalable software architectures, and collaborating with cross-functional teams to deploy production-grade systems.
Role Overview
Faculty seeks a Machine Learning Engineer based in London to bridge the gap between experimental research and real-world deployment. The professional in this position will handle architectural decisions, technical scoping, and the establishment of standards for scalable machine learning. Due to client work in the national security sector, candidates may need eligibility for UK Developed Vetting and occasional on-site client collaboration.
Key Responsibilities
Requirements
Skills and Tools
About the Company
Founded in 2014, Faculty builds and deploys responsible artificial intelligence for clients across global industries such as finance, government, retail, energy, life sciences, and defence. The organization operates a National Security and AI Safety business unit focused on responsible AI development and global stability. The firm employs human-centric approaches to transform client performance through technology.
How to Apply
Interested candidates should visit the official Faculty careers page to submit an application for this position.
Skills and tech stack
Questions candidates ask
Where is the job located?
The role is located in London, UK, though some on-site work with clients may be required from time to time.
What technical background is required for this position?
Candidates need strong Python skills, familiarity with software engineering best practices, understanding of the full machine learning lifecycle, and experience with frameworks like PyTorch, TensorFlow, or Scikit-learn.
Are there specific security clearance requirements?
Because of the nature of the work with government clients, applicants may need to be eligible for UK Developed Vetting (DV).
What does the interview process involve?
The evaluation consists of four stages: a 30-minute talent team screen, a 90-minute pair programming interview, a 90-minute system design interview, and a 60-minute commercial interview.
What cloud and container tools are used?
The position involves working with cloud platforms like AWS, Azure, and GCP, alongside container and orchestration tools such as Docker and Kubernetes.