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Machine Learning Engineer
Faculty · London, England · Posted 14 days ago
Build and deploy production-grade ML systems for pharma and MedTech clients, owning technical scoping and architecture decisions that shape how Faculty deploys ML at scale.
Bringing medicine to patients is complex, expensive and high-risk. Faculty’s Life Science’s team is concentrated on building AI solutions which optimise the research and commercialisation of life-changing therapies.
We partner with major pharma firms, academic research centres and MedTech start-ups to design and deliver solutions which address critical healthcare challenges, and help to democratise health for all.
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.
WHAT YOU'LL BE DOING:
- 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 Faculty’s standards for deploying machine learning at scale.
- Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.
WHO WE'RE LOOKING FOR:
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There are 94 open roles at Faculty right now. This role is one of 567 Machine Learning Engineer openings and centers on Python, Machine Learning, Scikit-learn, and Tensorflow.
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