We're looking for a Backend Engineer to help build and scale the Machine Learning Platform that powers how our company 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 our company: 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.
We believe three things matter for every role at our company: 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.
Requirements:
3+ 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.
3+ 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.
This position is open to all candidates.
