Machine Learning Engineer, Faculty

About the Role

Enabling clients to use cutting edge AI to improve the performance of their business

Who you are

• You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch
• You possess strong Python skills and solid experience in software engineering best practices
• You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security
• You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale
• You are comfortable with core ML concepts, including probability, statistics, and common learning techniques
• You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders
• You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions

If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours

What the job involves

• Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients
• You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices
• Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems
• Building and deploying production-grade ML software, tools, and infrastructure
• Creating reusable, scalable solutions that accelerate the delivery of ML systems
• Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges
• Leading technical scoping and architectural decisions to ensure project feasibility and impact
• Defining and implementing [Employer hidden]’s standards for deploying machine learning at scale
• Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders

Application process

• Talent Team Screen (30 minutes)
• Pair Programming Interview (90 minutes)
• System Design Interview (90 minutes)
• Commercial Interview (60 minutes)