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 high-stakes environments.
- 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 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 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 in London 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 intellectual curiosity is celebrated, and you'll have ample opportunities for growth while working on cutting-edge projects that shape the future of technology.
StudySmarter Expert Advice🤫
We think this is how you could land Hybrid ML Engineer - Real-World AI for Defence in London
✨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 a CV just can't.
✨Tip Number 2
Show off your skills! Prepare a portfolio of your projects, especially those related to machine learning. Having tangible examples of your work can really impress during interviews.
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by doing mock coding challenges and system design exercises. This will help you feel confident and ready to tackle any question thrown your way.
✨Tip Number 4
Apply through our website! We love seeing applications directly from candidates who are excited about what we do. 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 in London
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 those specific projects you've worked on!
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, less is often more!
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’re well-versed in the machine learning frameworks mentioned in the job description, like Scikit-learn, TensorFlow, and PyTorch. Brush up on how to operationalise models using these tools, as you might be asked to demonstrate your understanding during the technical interviews.
✨Showcase Your Python Skills
Since strong Python skills are a must, prepare to discuss your past projects where you've used Python for machine learning. Be ready to explain your code and the software engineering best practices you followed, as this will likely come up in the pair programming interview.
✨Understand Cloud Platforms
Familiarise yourself with cloud platforms like AWS, Azure, or GCP, especially their architecture and security aspects. You may be asked about your experience with these platforms, so having specific examples of how you've used them in previous roles will help you stand out.
✨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 outcomes rather than just the technical details. This will be crucial in the commercial interview.
