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Software Engineer, Collections Infra

Employment contract Full-time Hybrid work

Publication date: 01/09/2026

As a Software Engineer on Collections Infra, you’ll help scale the infrastructure behind Notion’s database block. Databases power views, boards, charts, forms, and the workflows customers use to run their companies; as Notion grows into larger enterprise deployments and AI-native work, those systems need to become faster, more reliable, and ready for much higher concurrency. This team sits between infrastructure and product, building backend architecture that improves the customer experience while becoming a platform other Notion teams can build on. You’ll work on problems like database load performance, microservices, high-volume writes from agents, and the flexibility that makes Notion databases powerful and hard to scale.

Responsibilities

Help scale the infrastructure behind Notion’s database block; Build backend architecture that improves the customer experience while becoming a platform other Notion teams can build on; Work on problems like database load performance, microservices, high-volume writes from agents, and the flexibility that makes Notion databases powerful and hard to scale; Use AI as a real engineering collaborator to learn unfamiliar systems faster, explore debugging paths, generate tests, summarize traces or logs, and pressure-test implementation plans; Contribute to making databases faster and more reliable at P95 scale, especially as customers build larger centralized databases with more concurrent usage; Partner with senior engineers and product-facing teams to evolve Collections as a platform

Requirements

Strong backend engineering fundamentals, with interest in performance, reliability, scalability, and systems that need to hold up under real customer load; Curiosity about distributed systems, microservices, and data-layer trade-offs; Practical comfort with JavaScript or TypeScript, or the ability to ramp quickly in a codebase where those tools matter; Grit and truth-seeking in technical problem solving; A self-guided learning style: use docs, code, teammates, and AI to build context, ask better questions, and turn ambiguity into steady progress

Nice to have

Experience in distributed systems, microservices, and data-layer trade-offs

Benefits

Equity; Benefits
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