At a Glance
- Tasks: Lead the development of cutting-edge AI systems and mentor junior engineers.
- Company: Join Faculty, a leader in human-centric AI with a diverse and innovative team.
- Benefits: Competitive salary, professional growth, and the chance to work with brilliant minds.
- Other info: Dynamic environment with opportunities for travel and collaboration on high-stakes projects.
- Why this job: Make a real impact in defence through ethical and reliable AI solutions.
- Qualifications: Experience in ML lifecycle, Python, and cloud platforms like AWS or Azure.
The predicted salary is between 36000 - 60000 £ 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 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 outside of our London base.
About the role
As a Senior Machine Learning Engineer, we'll look to you to lead development and deployment of cutting-edge AI systems for our diverse clients. You'll design, build, and deploy scalable, production-grade ML software and infrastructure that meets rigorous operational and ethical standards. This is an ambitious, cross-functional role requiring a blend of technical expertise, engineering leadership, and confident client-facing skills.
What you'll be doing:
- Leading technical scoping and architectural decisions for high-impact ML systems
- Designing and building production-grade ML software, tools, and scalable infrastructure
- Defining and implementing best practices and standards for deploying machine learning at scale across the business
- Collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities
- Acting as a trusted technical advisor to customers and partners, translating complex concepts into actionable strategies
- Mentoring and developing junior engineers, actively shaping our team's engineering culture and technical depth
Who we're looking for:
You understand the full ML lifecycle and have significant experience operationalising models built with frameworks like TensorFlow or PyTorch. You bring deep expertise in software engineering and strong Python skills, focusing on building robust, reusable systems. You have demonstrable hands-on experience with cloud platforms (e.g., AWS, Azure, GCP), including architecture, security, and infrastructure. You've extensive experience working with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale. You thrive in fast-paced, high-growth environments, demonstrating ownership and autonomy in driving projects to completion. You communicate exceptionally well, confidently guiding both technical teams and senior, non-technical stakeholders.
The Interview Process:
Talent Team Screen (30 minutes)
Pair Programming Interview (90 minutes)
System Design Interview (90 minutes)
Commercial Interview (60 minutes)
What we can offer you:
The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day. Faculty is the professional challenge of a lifetime. You'll be surrounded by an impressive group of brilliant minds working to achieve our collective goals. Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to Faculty, and you'll learn something new from everyone you meet.
Senior Machine Learning Engineer in London employer: Faculty AI
At Faculty, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters collaboration and innovation. With access to powerful computational resources and opportunities for professional growth through mentorship and teaching, our team thrives in a supportive environment that values intellectual curiosity. Located in the vibrant Old Street area, you will be part of a diverse team dedicated to transforming organisational performance through impactful AI solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Machine Learning Engineer in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Faculty AI or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Faculty AI.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Faculty AI.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Faculty AI that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Senior Machine Learning Engineer in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Faculty AI.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Faculty AI and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Faculty AI
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Faculty AI uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
