Lead Machine Learning Engineer

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 f