As a Machine Learning Engineer at Faculty, you’ll bring ML from lab to production, shaping scalable software and best practices. You’ll work across cross-functional teams and with government clients to deliver high-impact, production-grade ML systems. You’ll influence architectural decisions and standards, ensuring secure and trustworthy AI at scale. This role offers the chance to contribute to national security initiatives and responsible AI deployment at pace.
Pay / Benefits
Unlimited Annual Leave Policy
Private healthcare and dental
Enhanced parental leave
Family-Friendly Flexibility & Flexible working
Sanctus Coaching
Hybrid Working
Responsibilities
Build and deploy production-grade ML software, tools, and infrastructure
Create reusable, scalable ML solutions to accelerate delivery
Collaborate with engineers, data scientists, and commercial leads to solve client challenges
Lead technical scoping and architectural decisions for feasibility and impact
Define and implement standards for deploying ML at scale
Act as a technical advisor to customers and partners, translating complex ML concepts for stakeholders
Key requirements
Experience with end-to-end ML lifecycle and operationalising models using Scikit-learn, TensorFlow, or PyTorch
Strong Python programming skills and software engineering practices
Hands-on experience with cloud platforms (AWS, Azure, GCP) including architecture and security
Experience with Docker and Kubernetes for scalable applications
Solid understanding of core ML concepts (probability, statistics, common learning techniques)
Excellent communication and ability to advise non-technical stakeholders
Thrives in a fast-paced environment with autonomous scope ownership
We use essential cookies to make this site work. With your permission we would also like to use the optional cookies below. Nothing optional is set until you choose, and you can change your mind at any time from "Cookie settings" at the bottom of every page. Cookie policy.
Essential — keep the site working and secure, and remember the choice you make here. Always on.