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About the Role
Join Faculty as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for 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.
Responsibilities
- 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.
Requirements
- Understanding of the full machine learning lifecycle.
- Experience operationalizing models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
- 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 & Kubernetes to build and manage applications at scale.
- Comfort with core ML concepts, including probability, statistics, and common learning techniques.
- Excellent communication skills, able to guide technical teams and advise non-technical stakeholders.
- Ability to thrive in a fast-paced environment with autonomy to own scope, solve, and deliver solutions.
- Eligibility for UK Security Clearance (SC) may be required.
- Willingness to work on site with clients from time to time.
Skills
- Machine Learning
- Python
- Software Engineering
- Cloud Platforms (AWS, Azure, GCP)
- Docker
- Kubernetes
- Scikit-learn
- TensorFlow
- PyTorch
- Probability
- Statistics
Location
- On site with clients (potential requirement)
Work Type
- Full-time
- Part-time (negotiable)
About the Company
- Faculty was established in 2014 with the belief that AI would be the most important technology of our time.
- Faculty has worked with over 350 global customers to transform performance through human-centric AI.
- Faculty innovates, builds, and deploys responsible AI that makes a significant impact.
- Faculty possesses unparalleled depth of technical, product, and delivery expertise.
- Faculty serves clients across government, finance, retail, energy, life sciences, and defense sectors.
- Faculty's business and reputation are growing rapidly.
- Faculty seeks individuals with intellectual curiosity and a desire to build a positive legacy through technology.
- Faculty empowers employees to envision and implement powerful AI applications.
- The Public Services Business Unit is committed to leveraging AI for the benefit of individual citizens and the public good.
- Faculty's work includes informing strategic government decisions, optimizing the NHS, and reducing bureaucratic backlogs.
- Faculty leads impactful work in the government sector.
Equal Opportunity
- Faculty aims to grow the best team, not the most similar one, recognizing that diversity of individuals fosters diversity of thought.
- Diverse teams are known to deliver better work, relevant to the world.
- Faculty is united by deep intellectual curiosity and a desire to use abilities for measurable positive impact.
- Applications are strongly encouraged from people of all backgrounds, ethnicities, genders, religions, and sexual orientations.
- Candidates who do not meet all requirements but are excited by the role and bring key strengths are encouraged to apply.
- Faculty is open to conversations about part-time hours.
- Faculty permits the use of AI for research and interview preparation, but not for generating answers during live interviews.
- Faculty uses an AI note-taker (Metaview) in interviews, with an option to opt out.
- Every application is reviewed by a human; no decisions are made by AI.