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Remote, Oregon, United States
Remote only
Insurance is a $7T global industry built on professional labor: underwriting, claims, pricing, operations, and distribution. Kalepa is building AI that can perform that work.
We create professional-grade AI systems for insurers: software that can read, reason, decide, and act inside real workflows and operate over long-time horizons. Kalepa begins by augmenting professionals with the tools to succeed at their jobs. That earns us the right to learn from real decisions, capture decision traces, and automate what can be automated successfully over time.
Our products are already in production with leading insurers, driving measurable outcomes and expanding across functions. The opportunity is far broader than any single workflow: to build the AI operating system for decision-making across insurance. For people who want to work on AI that matters, in a sector where correctness, trust, and real-world impact count, Kalepa is just getting started.
Kalepa is backed by leading investors such as IA Ventures and Inspired Capital, and our team brings experience from Meta, Palantir, Block, Affirm, Google, Amazon, McKinsey, Mastercard, and Uber.
Our culture is shaped by everyday decisions. Our values help us make them thoughtfully and consistently, especially when tradeoffs are difficult. We learn from mistakes so our good decisions outweigh our bad ones and their benefits compound over time.
In addition to our NY office, we have remote employees speaking 10+ languages across the globe. But we invest in bringing our people together both for regional meetups and global offsites (2021 - Playa Del Carmen | 2022 - Rome | 2023 - Buenos Aires | 2024 - Lisbon | 2025 - Cartagena | 2026 - Tbilisi).
Kalepa’s culture isn’t for everyone, and that’s ok. But for the people who are a fit, they can’t imagine working elsewhere.
We are looking for engineers who enjoy going deep on hard problems and owning end-to-end outcomes. As a Staff Backend Engineer, you'll work at the intersection of AI and backend engineering and have major ownership and agency: design the systems, make architectural calls under ambiguity, and play a key role in setting the technical direction for the team.
You'll be building and scaling LLM-based systems that process billions of highly unstructured data points every day, extract precise information, and surface insights that insurers rely on for high-stakes decisions, along with the services, data models, and external-facing APIs that sit at the center of Kalepa. This is a problem the industry hasn't solved effectively yet, and you'll be working on cutting-edge challenges that require creativity and ambition. We're a small team where individual contributions have an outsized impact on both our technology and our business.
We differentiate our North America salary ranges using two geographical zones:
Zone 1 (New York and San Francisco) - $246,000 to $314,000*
Zone 2 (all other locations in the US and Canada) - $222,000 to $314,000*
We are primarily a remote company with employees across the US, Canada, and Europe. Our HQ is in New York city.
This team is a mix of software engineers owning Kalepa’s IT integration platform (public APIs, control plane) and forward-deployed engineers who embed with clients to integrate atop Kalepa’s APIs. The team is tasked with ensuring Kalepa’s deployments are complete, high-quality, and conducive to self-service. The team will define clear criteria for deployment quality and build processes that raise that quality standard for every client. Where the codebase today limits extensibility, they plan and refactor for sustainability.
Ownership areas: Kalepa’s Public APIs and external docs; auth/SSO/permissions; control plane & admin tooling for configuring clients; adapters into external systems that build against Kalepa’s Public API.
For US employees only:
*The salary range listed is an estimate and will vary based on a variety of factors. Final compensation will be determined during the offer stage based on relevant experience, performance during the interview process, and geographic location, and may therefore differ from the posted range.
