Machine Learning Engineer (Safety)

The role

You will build and deploy production-grade machine learning systems and tools. You will collaborate with clients and cross-functional teams to deliver bespoke AI solutions and reinforce AI safety.

What you will do

• Building and deploying production-grade ML software, tools, and infrastructure.
• Creating reusable, scalable solutions for ML systems and capability testing of Frontier AI models.
• Collaborating with engineers, data scientists, and commercial leads to solve client challenges.
• Leading technical scoping and architectural decisions.
• Defining and implementing standards for deploying machine learning at scale.
• Acting as a technical advisor to customers and partners.

Required qualifications

• Comfortable building LLM applications.
• Familiar with multi-agent harness tooling and AI Safety evaluation procedures.
• Strong Python skills.
• Solid experience in software engineering best practices.
• Hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.
• Experience with container and orchestration tools such as Docker and Kubernetes.
• Comfortable with core ML concepts, including probability, statistics, and common learning techniques.
• Understand the full machine learning lifecycle.
• Experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
• Excellent communicator able to guide technical teams and advise non-technical stakeholders.
• Thrive in a fast-paced environment.

About the company

Faculty was established in 2014 to transform performance through human-centric AI. They work with over 350 global customers across sectors including government, finance, retail, energy, life sciences and defence.