Senior Backend Engineer - ML Platform

Requirements

• 6+ years of software engineering experience, with a strong record of delivering high-scale, production-grade systems
• Strong proficiency in Python
• Hands-on experience with relational and NoSQL databases, and at least one major cloud platform (AWS, Azure, or GCP)
• Experience with training, testing, deploying, and monitoring real-time or near real-time ML models in production
• Fluent with AI-powered development tools like Cursor and Claude Code, and genuinely curious about what's next in GenAI, LLMs, and AI agents
• Familiarity with AI concepts like RAG, embeddings, mixture-of-experts, prompt crafting, and LLM context engineering - an advantage
• Sharp problem-solving instincts and the ability to move fast without cutting corners
• Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related field
• Ready to work in an office environment most days of the week

Additional Information

We're looking for a Senior Backend Engineer to help build and scale the Machine Learning Platform that powers how Lemonade uses AI across the business. You'll be part of the ML Platform team, designing the infrastructure that lets our data scientists move faster, ship smarter, and operate with confidence in production. We believe three things matter for every role at Lemonade: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work. Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools. In this role you'll Design and build the foundational ML platform and AI agents to accelerate data science model delivery across all business units Architect cloud-native microservices running on Kubernetes, using infrastructure-as-code to automate model deployment and management Own the end-to-end ML lifecycle, covering training, testing, deployment, and real-time monitoring Evaluate and choose the right tools and technologies based on workload demands and performance requirements Collaborate with engineering, data science, and product teams to keep ML projects aligned with business goals Identify and fix reliability, scalability, and performance gaps before they become problems

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