Senior Software Engineer, AI

Hedvig is rewriting the rules of insurance. We're proving it's possible to disrupt an entire industry while genuinely keeping people's best interests at heart.

AI is becoming a core part of how we deliver that value. Not as a buzzword, and not as a research project, but as a practical tool to make our members spend less time on troubles.

We're looking for a senior backend engineer to help us build the next generation of AI-powered product experiences at Hedvig. You'll work across our stack, collaborate closely with product and design, and play a key role in turning AI capabilities into reliable features our members actually benefit from.

Concretely, you might find yourself:

• Designing and shipping an LLM-powered service that helps triage incoming claims and drafts responses for human agents to review

• Collaborating with ML engineers on building evaluation and monitoring pipelines so we know our AI features are working — and catch it quickly when they're not

• Integrating model APIs into our Kotlin backend in ways that handle failure, cost, and latency gracefully

• Prototyping new ideas with product and design, and deciding which ones are worth shipping

• Helping the wider engineering team develop good judgment about where AI genuinely adds value — and where it doesn't

You'll spend most of your time designing systems, writing backend code, and shipping features. You do need to understand LLMs well enough to build reliable products with them.

• Backend services in Kotlin with Spring Boot

• GraphQL as our API layer

• Microservices on AWS (with some GCP)

• Web in Next.js on Vercel

• Native mobile in Swift and Kotlin

• LLM APIs from the major providers, with evals and observability we're actively investing in

You don't need to be an expert in all of this. We care more that you're comfortable navigating complex systems and picking up new technologies when the work requires it.

• 4+ years of professional backend software engineering experience

• Take ownership of projects and larger initiatives end-to-end — aligning with product, design, and other stakeholders, navigating trade-offs, and driving them to a shipped, measurable outcome

• Strong computer science fundamentals and a pragmatic approach to system design

• Hands-on experience building features with LLMs or other AI tools. That could be integrating model APIs, designing prompt-driven features, building eval or automation workflows, or shipping AI-powered internal tools. Depth matters more than scale

• Good judgment about when not to use AI. Some problems are better solved with a rule, a database query, or a conversation

• Motivated by solving real customer problems, not by the technology for its own sake