Compare the Market
Machine Learning Engineering Manager
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Machine Learning Engineering Manager
Function: Data & AI Solutions
Location: Hybrid, London office
Curious about what’s next? So are we. Join Compare the Market and help to make financial decision making a breeze for millions.
At Compare the Market, we’re a purpose-driven business powered by tech and AI. We’re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.
We’ve carved a meerkat-shaped niche and we’re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you’ll fit right in.
We’d love you to be part of our journey.
Compare the Market is at an inflection point. We are shifting from a business that answers data questions to one that builds the intelligence powering our AI systems and platform.
As a Machine Learning Engineering Manager in our Data & AI Solutions team, you will lead a team building and owning production systems at the heart of our AI platform. That means real-time inference APIs serving millions of customers, batch data products powering intelligent decision-making, and LLM-based agentic solutions spanning both customer-facing products and internal tools that shape how the wider team works. These are not simple systems - and building them well, keeping them healthy, and continuously raising the engineering bar is what this team exists to do.
You will set technical direction, develop your team, and work closely with data, product, commercial and engineering colleagues to ensure the intelligence your team builds has a real effect on the business.
Some of the great things you'll be doing:
Leading Delivery of ML and AI Systems
- Lead and develop a team of ML engineers, creating the conditions for them to deliver robust, scalable systems into production - spanning real-time inference APIs, batch data products, and LLM-based agentic solutions
- Collaborate closely with data scientists to move prototypes into high-quality production systems, maintaining quality and performance as complexity scales
- Drive team planning, estimation, and sprint delivery - ensuring projects are delivered on time and to a high standard
- Ensure every system your team ships connects to a real outcome and has a mechanism to improve over time
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Growing Your Team
- Develop your team of machine learning engineers through regular feedback, technical mentorship and honest career conversations
- Contribute to hiring, helping bring in people who combine technical rigour with curiosity and commercial awareness
- Foster a team environment where ambition and delivery reinforce each other
- Contribute to standards and practices across the wider Data & AI Solutions chapter
Technical Leadership
- Set the standard for ML and AI engineering quality, reproducibility, and production-readiness across your team - covering model pipelines, deployment tooling, system design, and lifecycle automation
- Take ownership of how the team approaches production health: knowing when a system is degrading, having a plan to address it, and ensuring monitoring and observability are built in from the start
- Contribute to the design and evolution of our ML and AI tooling and shared platform components
- Be hands-on in design sessions, code reviews, and architectural decisions where your input matters
- Guide your team through a fast-moving tooling landscape spanning classical ML, LLM-based systems, and agentic AI patterns - knowing when each is the right approach
- Champion responsible, auditable AI - particularly important in a regulated financial services environment where precision and explainability are non-negotiable
- Encourage and model the use of AI-assisted development tools within your team, and be actively curious about how automated coding and workflow tools can increase the pace and quality of your team's output
Stakeholder Partnership
- Work closely with data, product, commercial and engineering leads to translate strategic priorities into well-scoped work
- Represent your team's work and capability to senior stakeholders, building confidence in what the team delivers and how it operates
- Help shape platform direction by feeding back requirements from applied ML delivery
Get help with your application
Your very own career expert that helps elevate your application to the next level.
What we'd like to see from you:
The Essentials
- Experience leading engineering teams focused on machine learning, ML platforms and AI systems
- Proven track record deploying and managing ML and AI systems in production at scale - including real-time inference, batch data products, and LLM-based solutions
- Strong Python and ML and AI engineering fundamentals - sufficient to assess your team's work and contribute directly when needed
- Understanding of infrastructure-as-code and CI/CD for ML systems (e.g. Terraform, GitHub Actions, ArgoCD)
- Clear communication: able to make technical work legible to commercial and product audiences
- Experience working in agile environments with clear accountability to delivery
- A degree in a quantitative discipline, or equivalent experience with production ML and AI systems - we are interested in what you can do, not where you studied
Highly Valued
- Hands-on experience with LLM-based systems: prompt engineering, RAG, tool use, or orchestration frameworks such as LangGraph or LangChain
- Familiarity with multi-step AI patterns - building systems where models plan, retrieve information, and take sequences of actions
- Understanding of experimentation at scale and the infrastructure needed to run it well
- Experience building or managing internal ML platforms, experimentation frameworks, or feature stores
- Interest in responsible AI and model governance practices
Why Compare the Market?
We’re a business built for pace and performance. Here, you’ll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress.
We believe diverse teams make better decisions, and we’re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.
If you’re ready to stretch yourself, raise the bar, and grow with a team that’s serious about performance, innovation, and purpose, we’d love to hear from you.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location