Backend Engineer (Europe)
Kalepa
- Work on real product and R&D systems at the intersection of software engineering and applied AI
- Own backend services, data and ML pipelines, and the infrastructure behind the product
- Build and improve systems that ingest, process, and reason over large volumes of structured and unstructured data
- Collaborate closely with experienced full-stack, backend, ML, and platform engineers
- Shape how production systems are designed, built, tested, and operated at scale
Zahtevi

Potrebne kvalifikacije
About Kalepa:
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 Values (This is important):
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.
- Excellence - We take pride in our craft and expect excellent outcomes in everything we do. We set high standards, verify our work, and own the result. We make deliberate tradeoffs, including rethinking an approach, redoing work, or investing more time.
- Pursuit of the Truth - We seek the truth, even when it is uncomfortable. We question assumptions, give candid feedback, and change our minds when evidence warrants it. We work with customers to understand their challenges and what solving them requires.
- Ensuring Customer Success - We take responsibility for delivering results that matter to our customers. We measure success through improvements in their growth, profitability, and efficiency. We challenge our approach and customers’ requests when necessary, remaining accountable for the outcome.
- First Principles Thinking - We reason from a problem’s underlying facts, objectives, and constraints. We distinguish what is necessary from what is familiar and question assumptions that limit possible solutions. We test our reasoning against reality and address root causes with approaches that scale without unnecessary complexity.
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.
About the role:
Salary range: $80k – $105k USD*
We are looking for smart engineers who love to code, seek challenging problems, and appreciate recognition for excellent work.
As a core engineer at Kalepa, you'll work on real product and R&D systems at the intersection of software engineering and applied AI. You will own backend services, data and ML pipelines, and the infrastructure behind our product used by commercial insurance underwriters.
You'll build and improve systems that ingest, process, and reason over large volumes of structured and unstructured data, including documents, third-party sources, and model outputs. This is hands-on engineering work on problems that matter to the business, with ownership of systems end to end.
You'll collaborate closely with experienced full-stack, backend, ML, and platform engineers, and you'll be expected to shape how production systems are designed, built, tested, and operated at scale. We're a small team where individual contributions have an outsized impact on both our technology and our business.
You'll Be a Good Fit If You:
- Have 3+ years of professional software engineering experience building and shipping production systems
- Have excellent development skills including design, debugging, and problem solving
- Are strong in Python, with solid fundamentals in at least one other language
- Have worked with relational databases, with a preference for PostgreSQL
- Have experience with AWS and asynchronous or serverless workflows (EC2, Lambda, etc.)
- Can design and build data-driven microservices leveraging RESTful APIs
- Have a degree in computer science, mathematics, engineering, or a related field, or equivalent practical experience
- Enjoy writing code and solving non-trivial technical problems
- Are curious about AI, data, and real-world applications of machine learning
- Take ownership and are comfortable working with ambiguity
- Move fast, learn quickly, and value high standards
- Appreciate direct feedback, meritocracy, and transparency
What We Offer:
- Competitive salary based on experience level*
- Significant equity options package
- Direct work on core systems at a fast-growing AI startup
- Fully remote work from Europe
- Onsite team events in Warsaw and global team offsites
- Real responsibility and production impact from day one
- 20 days of PTO a year
- Gym, phone, and learning stipend reimbursement
* 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.
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