Machine Learning Engineer - TWE

We are hiring a deeply technical AI Engineer to build production-grade ML systems that power real insurance claims decisions.

We are supporting a Seed-stage company from the latest cohort of Y Combinator (S24), backed by leading global investors and scaling rapidly across London.

They are not building AI demos. They are embedding AI into insurer operations at scale.

• Designing and deploying AI systems that directly impact claims accuracy and speed.
• Building production LLM-powered workflows (Lang Chain, orchestration frameworks, evaluation layers).
• Implementing classic ML/statistical models where appropriate. This is not prompt engineering-only.
• Building backend components required to product ionise AI systems.
• Defining metrics, evaluation frameworks, and performance monitoring.

We care deeply about applied ML rigour — not buzzwords.

There are many people who call themselves “AI Engineers.”

• 2+ years delivering ML systems from concept to production.
• Deep understanding of classical statistics and machine learning fundamentals.
• Clear thinking around evaluation metrics, bias, performance drift, and trade-offs.
• Comfort discussing model architecture, feature engineering, and system design.
• Ability to build backend systems supporting ML deployment.
• High-intensity, office-based.
• We hire slowly and expect alignment with startup realities.
• Strong ownership mindset required.

You will work directly with founders who have already built and exited in the insurance space and have experience operating in regulated environments.

• Engineers who want to build AI-native systems from first principles.
• Former early employees or founding engineers.
• Builders who care more about impact and ownership than job titles.
• People aligned with the intensity of venture-backed startups.

If you want ownership, velocity, and to help build the AI-native backbone of insurance, we want to speak with you.