Machine Learning Engineer

Overview

Join Faculty's Life Sciences team as a Machine Learning Engineer to deliver bespoke, production-grade AI solutions for clients in pharma, academic research and MedTech.

What you'll do

• Build and deploy production-grade ML software, tools, and infrastructure.
• Create reusable, scalable solutions that accelerate the delivery of ML systems.
• Collaborate with engineers, data scientists, and commercial leads to solve critical client challenges.
• Lead technical scoping and architectural decisions to ensure project feasibility and impact.
• Define and implement Faculty's standards for deploying machine learning at scale.
• Act as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.

What you'll need

• Understanding of the full machine learning lifecycle and experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
• Strong Python skills and 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 to build and manage applications at scale.
• Comfort with core ML concepts, including probability, statistics, and common learning techniques.
• Excellent communication, able to guide technical teams and confidently advise non-technical stakeholders.
• Thrives in a fast-paced environment and enjoys the autonomy to own scope, solve and deliver solutions.

Details

• Location: London.
• Interview process: Talent Team Screen (30 minutes), Pair Programming Interview (90 minutes), System Design Interview (90 minutes), Commercial Interview (60 minutes).
• Part-time hours are open for discussion.