Lead Machine Learning Engineer at Zego

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

28 May, 26

Salary

0.0

Posted On

27 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

ML Engineering, Model Training, Model Deployment, Model Monitoring, Feature Engineering, ML Platform, Python, Pandas, Scikit-learn, DevOps, Kubernetes, CI/CD, Docker, GitOps, AWS, SQL

Industry

Insurance

Description

At Zego, we know that traditional motor insurance holds good drivers back. It's too complicated, too expensive, and it doesn't take into account how well you actually drive. That's why, since 2016, we've been on a mission to change all of that. Our mission at Zego is to offer the lowest-priced insurance for good drivers. From van drivers and gig workers to everyday car drivers, our customers are our driving force — they're at the heart of everything we do. We've sold tens of millions of policies so far, and raised over $200 million in funding. And we're only just getting started. Who we're looking for We're looking for a Lead Machine Learning Engineer to join our ML Platform team. You'll own and evolve the platform, tooling, and infrastructure that powers ML across Zego making it easier and faster for the business to build, deploy, and monitor predictive models at scale. Today, the platform primarily supports our pricing models. That scope is growing telematics and other areas of the business are next, and you'll play a central role in shaping what that looks like. We're looking for someone who's strong across the full ML lifecycle: comfortable building reliable infrastructure and capable of getting hands-on with modelling when the opportunity is there. You'll be joining a small team reporting to the Head of Machine Learning Engineering. There's genuine scope to influence how ML is done at Zego. Key Responsibilities Design and build end-to-end ML systems from feature engineering through to model training, deployment, and monitoring Develop and maintain the ML platform and tooling that enables the team (and the wider business) to ship models efficiently and reliably Build and improve model lifecycle tooling: deployment, monitoring, alerting, and retraining for predictive models across multiple domains Help extend the platform to new use cases and data domains as the team's scope grows Collaborate closely with Data Scientists and Product teams to translate business problems into well-scoped ML solutions Communicate complex technical concepts to both technical and non-technical stakeholders — clear thinking, clear storytelling Required Skills ML engineering experience: You've built, trained, and deployed production ML models, not just managed pipelines. You're comfortable across the full lifecycle, from experimentation to serving at scale. Strong fundamentals: Solid grounding in machine learning techniques, you know when to reach for a GLM, when a gradient-boosted tree will do, and when something more complex is warranted. Python: You've built production-grade Python applications and are fluent in the ML/data ecosystem (pandas, scikit-learn, and the usual suspects). Platform & infrastructure: Hands-on experience with DevOps practices, Kubernetes, CI/CD, Docker, GitOps. You care about reliability and developer experience, not just model accuracy. Cloud (AWS): You've worked in cloud environments, ideally AWS. SQL: Strong SQL skills, particularly with cloud data warehouses (we use Snowflake). Communication & collaboration: You translate ambiguous business needs into clear, actionable technical work. You've thrived in cross-functional teams with Data Scientists and Product Managers. Technical leadership: You raise the bar for those around you through code reviews, design decisions, mentoring, and setting good engineering standards. Nice To Have Experience in UK motor insurance or insurtech Exposure to LLMs or LLMOps in a practical, production context Experience with infrastructure-as-code (we use Terraform) Experience with gRPC / protobuf How we work We believe that teams work better when they have time to collaborate and space to get things done. We call it Zego Hybrid. While some of our team choose to come into our central London office once a week, we're flexible — some people prefer being in once a month or even quarterly. It's all about finding the right balance between collaborative face time and focused home-working, so we can achieve great results while maintaining a healthy work-life balance. This role can be based in London or Portugal. We do expect you to be available for in-person collaboration when it matters — team days, workshops, and key meetings and we cover the travel costs for company-wide events. What's it like to work at Zego? Joining Zego is a career-defining move. People go further here, reaching their full potential to achieve extraordinary things. We're spread throughout the UK and Europe, and united by our drive to get things done. We're proud of our company and our culture – a friendly and inclusive space where we can lift each other up and celebrate our wins every day. Together, we're setting the bar higher, delivering exceptional work that makes a difference. Our people are the most important part of our story, and everyone here plays a role. There's loads of room to learn and grow, and you'll get the freedom to steer your career wherever you want. You'll work alongside a talented group who embrace each other's differences and aren't afraid of a challenge. We recognise our achievements, learn from our mistakes, and help each other to be the best we can be. Together, we're making insurance matter. Benefits We reward our people well. Join us and you'll get a market-competitive salary, private medical insurance, company share options, generous holiday allowance, and a whole lot of wellbeing benefits. And that's just for starters. We're an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, marital status, or disability status.

Responsibilities

The role involves designing and building end-to-end Machine Learning systems, covering everything from feature engineering through to model deployment and monitoring. A key focus is developing and maintaining the ML platform and tooling to enable efficient and reliable model shipping across the business.