At a Glance
- Tasks: Build and deploy impactful AI solutions for diverse clients in a hybrid role.
- Company: Join Faculty, a leader in responsible AI innovation since 2014.
- Benefits: Enjoy unlimited leave, private healthcare, and flexible working arrangements.
- Other info: Diverse team culture with excellent career growth and support for Women in Data.
- Why this job: Make a real-world impact with cutting-edge machine learning technology.
- Qualifications: Experience in machine learning frameworks and strong Python skills required.
The predicted salary is between 43200 - 72000 € per year.
Location: London - Hybrid
We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we've worked with over 350 global customers to transform their performance through human-centric AI.
We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.
Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch-defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen.
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.
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 & Kubernetes to build and manage applications at scale.
- You are comfortable with core ML concepts, including probability, statistics, and common learning techniques.
- You’re an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders.
- You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and deliver solutions.
Our Recruitment Ethos
We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We’re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations.
Some of our standout benefits:
- Unlimited Annual Leave Policy
- Private healthcare and dental
- Enhanced parental leave
- Family-Friendly Flexibility & Flexible working
- Sanctus Coaching
- Hybrid Working (2 days in our Old Street office, London)
If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please do apply or reach out to our Talent Acquisition team for a confidential chat - talent@faculty.ai. Please know we are open to conversations about part-time roles or condensed hours.
We are proud supporters of Women in Data. Connect, engage and belong to the largest free female data community in the UK – visit: www.womenindata.co.uk to join our community.
Stay connected! Follow us on LinkedIn for updates on career opportunities and more.
Machine Learning Engineer in Leeds employer: Women in Data®
At Faculty, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. Our commitment to employee growth is reflected in our unlimited annual leave policy, private healthcare, and family-friendly flexibility, ensuring that our team can thrive both personally and professionally. Join us to be part of a diverse and intellectually curious environment where your contributions will have a meaningful impact on the future of AI in healthcare and beyond.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer in Leeds
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with Faculty employees on LinkedIn. Building relationships can open doors that a CV just can't.
✨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 those interviews! Brush up on your technical knowledge and be ready to discuss your experience with frameworks like TensorFlow or PyTorch. Practising common interview questions can help you feel more confident.
✨Tip Number 4
Don't forget to apply through our website! It's the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining Faculty and being part of our mission.
We think you need these skills to ace Machine Learning Engineer in Leeds
Some tips for your application 🫡
Show Your Passion for AI:When writing your application, let your enthusiasm for AI shine through! We want to see how you connect with the technology and its potential impact on the world. Share any personal projects or experiences that highlight your passion.
Tailor Your CV and Cover Letter:Make sure to customise your CV and cover letter for the Machine Learning Engineer role. Highlight relevant skills and experiences that align with our job description. We love seeing how your background fits into our mission!
Be Clear and Concise:Keep your application straightforward and to the point. Use clear language to describe your experiences and achievements. We appreciate a well-structured application that makes it easy for us to see your qualifications.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at Women in Data®
✨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 as you go.
✨Understand Cloud Platforms
Familiarise yourself with cloud platforms like AWS, Azure, or GCP. Be prepared to discuss your experience with architecture and security in these environments, as well as how you've used container tools like Docker and Kubernetes in your previous roles.
✨Communicate Clearly
As an excellent communicator, you'll need to translate complex ML concepts for non-technical stakeholders. Practice explaining your past projects in simple terms, focusing on the impact and value they brought to clients. This will show that you can bridge the gap between technical and non-technical teams.
