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- Tasks: Build and deploy impactful machine learning solutions for diverse clients.
- Company: Join Faculty, a leader in human-centric AI transformation.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Why this job: Work with brilliant minds and make a real-world impact with AI.
- Qualifications: Experience in machine learning frameworks and strong Python skills required.
- Other info: Dynamic team environment with excellent career advancement opportunities.
The predicted salary is between 36000 - 60000 ÂŁ per year.
About Faculty
At Faculty, we transform organisational performance through safe, impactful and human‑centric AI. With more than a decade of experience, we provide over 350 global customers with software, bespoke AI consultancy, and Fellows from our award winning Fellowship programme. Our expert team brings together leaders from across government, academia and global tech giants to solve the biggest challenges in applied AI. Should you join us, you’ll have the chance to work with, and learn from, some of the brilliant minds who are bringing Frontier AI to the frontlines of the world.
About the team
Our Retail and Consumer experts are dedicated to helping clients in an industry which is being transformed by new technologies and evolving consumer expectations. Leveraging over a decade of experience in Applied AI, we combine exceptional technical and delivery expertise to empower businesses to adapt and thrive.
About the role
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:
- 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 as Docker.
Machine Learning Engineer employer: Faculty
Contact Detail:
Faculty Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with Faculty employees on LinkedIn. A personal connection can make all the difference when it comes to landing that interview.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, having tangible examples of your work can really impress potential employers.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and ML concepts. Practice common algorithms and be ready to discuss your past projects in detail. We want to see how you think and solve problems!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are genuinely interested in joining our team at Faculty.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with frameworks like Scikit-learn, TensorFlow, or PyTorch, and don’t forget to showcase your Python skills and software engineering best practices.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI and how your background makes you a perfect fit for Faculty. Be sure to mention any relevant projects or experiences that demonstrate your ability to deliver impactful AI solutions.
Showcase Your Projects: If you’ve worked on any machine learning projects, make sure to include them in your application. Whether they’re personal projects or professional work, showcasing your hands-on experience will help us see your practical skills in action.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status directly. Plus, we love seeing applications come through our own platform!
How to prepare for a job interview at Faculty
✨Know Your ML Frameworks
Make sure you brush up on your knowledge of frameworks like Scikit-learn, TensorFlow, and PyTorch. Be ready to discuss how you've operationalised models in the past and share specific examples of projects where you’ve used these tools.
✨Showcase Your Python Skills
Since strong Python skills are a must, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, efficient code and be ready to explain your thought process.
✨Understand Cloud Platforms
Familiarise yourself with cloud platforms like AWS, Azure, or GCP. Be prepared to discuss your experience with cloud infrastructure, architecture, and security, as well as how you’ve deployed machine learning solutions in these environments.
✨Communicate Complex Concepts Simply
As a Machine Learning Engineer, you'll need to translate complex ML concepts for stakeholders. Practice explaining your work in layman's terms, focusing on how your solutions can impact the business and solve client challenges.
