Machine Learning Engineer

Stack

Responsibilities

• ▹Building and deploying production-grade ML software, tools and infrastructure
• ▹Creating reusable, scalable solutions that accelerate delivery of ML systems
• ▹Collaborating with engineers, data scientists and commercial leads to solve client challenges
• ▹Leading technical scoping and architectural decisions for project feasibility
• ▹Defining and implementing Faculty's standards for deploying ML at scale
• ▹Acting as a technical advisor to customers, translating complex ML concepts

Requirements

• ▹Understanding of the full ML lifecycle and experience operationalising models with Scikit-learn, TensorFlow or PyTorch
• ▹Strong Python skills and solid software engineering best practices
• ▹Hands-on experience with cloud platforms (AWS, Azure, GCP), including architecture and security
• ▹Experience with container and orchestration tools such as Docker and Kubernetes
• ▹Comfort with core ML concepts including probability, statistics and learning techniques

Soft skills

What we offer

• ▹Unlimited Annual Leave Policy
• ▹Private healthcare and dental
• ▹Enhanced parental leave
• ▹Family-friendly flexibility and flexible working
• ▹Sanctus Coaching
• ▹Hybrid working

About the company

Faculty's Life Sciences team builds AI solutions that optimise the research and commercialisation of life-changing therapies, partnering with major pharma firms, academic research centres and MedTech start-ups.