Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead the design and development of cutting-edge machine learning platforms for major clients.
- Company: Fast-scaling AI organisation transforming retail with innovative ML solutions.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Why this job: Shape impactful AI projects and enhance customer experiences in a dynamic environment.
- Qualifications: Strong ML engineering experience, proficiency in Python, and familiarity with cloud infrastructure.
- Other info: Mentorship opportunities and a chance to influence best practices in ML system design.
The predicted salary is between 43200 - 72000 £ per year.
We’re working with a fast-scaling AI organisation that partners with large product and platform-led businesses to deliver machine learning systems that support personalisation, demand forecasting, and operational resilience. Their work helps clients enhance customer experience, optimise fulfilment and logistics, and make smarter, data-driven decisions.
This is a senior-level technical role with plenty of scope to shape the architecture, tooling, and delivery practices across impactful applied AI projects.
What You’ll Be Doing
- Lead the design and development of robust machine learning platforms that power core business functions across multiple client environments.
- Set technical strategy across projects and drive execution on model development, deployment workflows, and infrastructure.
- Collaborate with engineers, product teams, and stakeholders to translate high-level objectives into scalable ML solutions.
- Build shared tools and frameworks that support consistency and reusability across the engineering function.
- Guide project scoping and delivery plans while helping define best practices around ML system design.
- Mentor engineers at varying levels and contribute to hiring, capability building, and tooling decisions.
- Help clients understand trade-offs and solution architecture, acting as the senior point of technical contact throughout delivery.
What They’re Looking For
- Strong experience leading end-to-end ML engineering projects, ideally with exposure to customer-centric, high-traffic environments.
- Proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Familiarity with cloud-based deployment workflows and infrastructure management (e.g. AWS, Azure, or GCP).
- Real-world experience working with Docker, Kubernetes, and production ML pipelines.
- Strong communication skills and comfort engaging across technical, commercial, and executive teams.
- A pragmatic and detail-oriented approach to balancing experimentation with reliable, scalable delivery.
If this role interests you and you would like to find out more (or find out about other roles), please apply here or contact us via niall.wharton@Xcede.com (feel free to include a CV for review).
Lead Machine Learning Engineer in City of London employer: Xcede
Contact Detail:
Xcede Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Machine Learning Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's GitHub repos or a personal website, having tangible examples of your work can really set you apart from the competition.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and frameworks. Practice explaining your past projects and how they relate to the role you're applying for. Confidence and clarity can make a huge difference!
✨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 hearing from passionate candidates who are eager to join our team and make an impact.
We think you need these skills to ace Lead Machine Learning Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Lead Machine Learning Engineer role. Highlight your experience with ML frameworks and cloud-based deployments, as these are key for us.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about machine learning and how your background makes you a great fit. Be specific about your achievements and how they relate to the job description.
Showcase Your Projects: If you've worked on relevant projects, whether in a professional or personal capacity, make sure to mention them. We love seeing real-world applications of your skills, especially in customer-centric environments.
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!
How to prepare for a job interview at Xcede
✨Know Your Tech Inside Out
Make sure you’re well-versed in the machine learning frameworks mentioned in the job description, like PyTorch and TensorFlow. Brush up on your Python skills and be ready to discuss your past projects in detail, especially those that involved high-traffic environments.
✨Showcase Your Leadership Skills
As a Lead Machine Learning Engineer, you'll need to demonstrate your ability to lead projects and mentor others. Prepare examples of how you've guided teams in the past, tackled challenges, and contributed to the technical strategy of your previous roles.
✨Understand the Business Impact
This role is all about delivering value to clients. Be prepared to discuss how your work has enhanced customer experience or optimised processes in previous positions. Show that you can translate technical objectives into business outcomes.
✨Engage with Stakeholders
Strong communication skills are key for this position. Think of examples where you've successfully collaborated with engineers, product teams, or even executive stakeholders. Practice explaining complex technical concepts in simple terms to show your versatility.
