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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

Understand the full machine learning lifecycle and have experience operationalizing models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
Possess strong Python skills and solid experience in software engineering best practices.
Bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.
Have worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale.
Comfortable with core ML concepts, including probability, statistics, and common learning techniques.
Excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders.
Thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions.
Eligible for UK Developed Vetting (DV) and willing 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
MLOps

Location

On-site (from time to time)

Work Type

Hybrid Working
Part-time hours considered

Experience Level

Mid-level

Benefits

Unlimited Annual Leave Policy
Private healthcare and dental
Enhanced parental leave
Family-Friendly Flexibility
Flexible working
Sanctus Coaching

About the Company

Faculty was established in 2014 with the vision that AI would be the most important technology of our time.
Faculty has worked with over 350 global customers to transform their performance through human-centric AI.
Faculty innovates, builds, and deploys responsible AI that makes a significant impact.
Faculty brings unparalleled depth of technical, product, and delivery expertise to clients across government, finance, retail, energy, life sciences, and defense.
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 the most powerful applications of AI.
The National Security and AI Safety business unit is dedicated to advancing the responsible development and deployment of AI in support of national security and global stability.
This unit works on strengthening mission-critical capabilities across national security and intelligence, and partners with frontier labs for AI safety red teaming and evaluation.
The unit operates at the frontier of high-stakes, high-impact missions.
Faculty partners with Government and major tech organizations to ensure AI is transformative, secure, trustworthy, and safe.

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 deliver better work, relevant to the world.
The team 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.