At a Glance
- Tasks: Build and deploy impactful AI solutions for diverse clients in the defence sector.
- Company: Join Faculty, a leader in human-centric AI innovation since 2014.
- Benefits: Enjoy unlimited annual leave, private healthcare, and flexible working options.
- Other info: Diverse team culture that values intellectual curiosity and positive impact.
- Why this job: Make a real-world impact with cutting-edge AI technology in a fast-paced environment.
- Qualifications: Experience in machine learning, Python, and cloud platforms like AWS or Azure.
The predicted salary is between 60000 - 80000 £ per year.
Why Faculty? 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.
About the team: Our Defence team is focused on building and embedding human-centered AI solutions which give our nation a competitive edge in the defence sector. We collaborate with our clients to bring ethical, reliable and cutting-edge AI to high-stakes situations and maintain the balance of global powers essential to our liberty.
Because of the nature of the work we do with our Defence clients, you will need to be eligible for UK Security Clearance (SC) and willing to work up to three days per week on-site with these customers which may require travel to locations throughout the UK. When not required on client sites, you’ll have the flexibility to work from our London office or remotely from elsewhere within the UK.
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 at 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 Interview Process:
- Talent Team Screen (30 minutes)
- Pair Programming Interview (90 minutes)
- System Design Interview (90 minutes)
- Commercial Interview (60 minutes)
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
If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.
Hybrid ML Engineer - Real-World AI for Defence employer: Faculty
At Faculty, we pride ourselves on being at the forefront of AI innovation, empowering our employees to make a meaningful impact in the defence sector. With a strong emphasis on work-life balance, our unlimited annual leave policy and family-friendly flexibility ensure that you can thrive both personally and professionally. Join a diverse team in London where your growth is supported through collaboration and cutting-edge projects, all while contributing to responsible AI solutions that shape the future.
StudySmarter Expert Advice🤫
We think this is how you could land Hybrid ML Engineer - Real-World AI for Defence
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at Faculty or similar companies. A friendly chat can open doors that applications alone can't.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or contributions to open-source, make sure to highlight them. Real-world examples of your work can speak volumes.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss how you've tackled challenges in past projects. Confidence and clarity are key!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team.
We think you need these skills to ace Hybrid ML Engineer - Real-World AI for Defence
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 our mission of building responsible AI that makes a real-world impact.
Tailor Your Experience:Make sure to highlight your relevant experience in machine learning and software engineering. We love seeing how your skills align with the role, so don’t hold back on showcasing your projects and achievements!
Be Clear and Concise:Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon and focus on communicating your ideas effectively. Remember, we’re looking for great communicators!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity with our Defence team!
How to prepare for a job interview at Faculty
✨Know Your ML Frameworks
Make sure you brush up on your experience with frameworks like Scikit-learn, TensorFlow, and PyTorch. Be ready to discuss specific projects where you've operationalised models, as this will show your practical understanding of the machine learning lifecycle.
✨Showcase Your Python Skills
Since strong Python skills are a must, prepare to demonstrate your coding abilities during the pair programming interview. Practise common algorithms and data structures in Python, and be ready to explain your thought process while coding.
✨Understand Cloud Platforms
Familiarise yourself with cloud platforms like AWS, Azure, or GCP. Be prepared to discuss how you've used these platforms for deploying ML solutions, including any architectural decisions you've made regarding security and scalability.
✨Communicate Effectively
As an excellent communicator, you'll need to translate complex ML concepts for non-technical stakeholders. Practise explaining your past projects in simple terms, focusing on the impact and value they brought to clients, which will help you shine in the commercial interview.