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- Tasks: Lead the design and build of advanced machine learning platforms for energy systems.
- Company: AI advisory group transforming infrastructure and energy with innovative ML solutions.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Why this job: Join a top-tier team making a real impact in the consumer energy sector.
- Qualifications: Strong ML engineering leadership, proficient in Python, and cloud deployment experience.
- Other info: Exciting chance to mentor and grow within a dynamic, tech-driven environment.
The predicted salary is between 43200 - 72000 £ per year.
This AI advisory group partners with large-scale operators across infrastructure, energy, and environmental systems to design and deploy applied machine learning solutions. Their mission is to unlock measurable value across national networks, complex supply systems, and fast-evolving green technologies.
You’ll join a technically elite team working on high-impact use cases in the consumer energy space, helping major organisations optimise decision-making, automate processes, and unlock predictive insights at scale.
What You’ll Be Doing
- Lead the technical design and build of complex machine learning platforms that operate across distributed, high-volume energy systems.
- Scope out engineering roadmaps for challenging projects and drive delivery across multiple technical workstreams.
- Architect and scale ML-driven platforms, ensuring clean integration, maintainability, and performance in live environments.
- Build shared tooling and infrastructure that supports long-term platform growth across teams and clients.
- Provide senior input on system design decisions, balancing exploratory freedom with robust technical frameworks.
- Take ownership of infrastructure decisions, ensuring consistency across development, deployment, and observability layers.
- Act as a trusted technical advisor, guiding clients and internal teams through applied ML challenges from concept to production.
- Play a key role in growing internal engineering capability, contributing to hiring, mentoring, and technical development.
What They’re Looking For
- Strong engineering leadership experience in ML-focused environments, with a focus on productionisation and long-term scalability.
- Proficient in Python, with hands-on experience using frameworks like PyTorch, TensorFlow, or scikit-learn in applied settings.
- Proven background in deploying ML systems on cloud platforms such as AWS, Azure, or GCP.
- Familiar with containerisation and orchestration tools such as Docker and Kubernetes.
- Experienced in working across ambiguous or high-risk environments, with the ability to structure delivery and define scope.
- Strong communication skills, with confidence engaging senior stakeholders and translating between technical and strategic goals.
- Bonus: exposure to systems built for large-scale energy or infrastructure use cases, especially those involving real-time optimisation or data-rich forecasting.
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 London employer: Xcede
Contact Detail:
Xcede Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage with online communities. The more people you know, the better your chances of landing that Lead Machine Learning Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving Python and frameworks like PyTorch or TensorFlow. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with cloud platforms and containerisation tools, as well as how you've tackled complex ML challenges in the past.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Lead Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the job description. Highlight your experience with machine learning platforms and any relevant projects you've led. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about the role and how your background makes you a perfect fit. Don’t forget to mention your experience in energy systems if you have it!
Showcase Your Technical Skills: Be specific about your technical expertise, especially in Python and ML frameworks like PyTorch or TensorFlow. We love seeing hands-on experience, so don’t hold back on those details that demonstrate your capabilities.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications better and ensures you get noticed by the right people. Plus, it’s super easy!
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. Be ready to discuss your hands-on experience with these tools and how you've applied them in real-world scenarios.
✨Showcase Your Leadership Skills
As a Lead Machine Learning Engineer, you'll need to demonstrate strong engineering leadership. Prepare examples of how you've led teams or projects, especially in high-risk environments, and be ready to discuss how you balance exploratory freedom with technical frameworks.
✨Understand the Business Impact
This role is all about unlocking value for clients. Brush up on how machine learning can optimise decision-making and automate processes in the energy sector. Be prepared to share insights on how your work has positively impacted previous projects or organisations.
✨Engage with Stakeholders
Strong communication skills are key. Think about how you can translate complex technical concepts into strategic goals for senior stakeholders. Practice articulating your ideas clearly and confidently, as this will show your ability to act as a trusted advisor.
