Software Engineer, Superhuman Database Infrastructure

Superhuman Superhuman · Consumer · Hub - Seattle · Engineering, Product, Design, and Marketing

Software Engineer to join the Platform team within Superhuman Docs, working on the structured data storage layer that powers tables, grids, and views. This role involves contributing to and growing into a technical leadership position on the foundation of the platform, focusing on aspects like cell storage, row ordering, index management, view materialization, and query execution. The engineer will also drive improvements in handling large, complex tables at scale, optimize server-side query planning, collaborate with other teams to ensure correctness, contribute to performance benchmarking, and help define the evolution of structured data on the platform.

What you'd actually do

  1. Contribute to — and grow into technical leadership on — the structured data storage layer, including cell storage, row ordering, index management, view materialization, and query execution.
  2. Help drive improvements to how the platform handles large, complex tables at scale: lazy hydration strategies, data compaction, cache invalidation, and the lifecycle of cell data through the sync pipeline.
  3. Work on server-side query planning for database-backed tables — pushing filter, sort, and grouping operations closer to the data layer and away from the client.
  4. Collaborate with the formula engine and sync teams to ensure correctness at the boundaries: formula dependency tracking, column state transitions, and consistent view state across concurrent operations.
  5. Contribute to performance benchmarking and profiling practices for table operations, giving the team observable data to drive storage and query optimization decisions.

Skills

Required

  • 8+ years of industry experience
  • 5+ years of experience in relevant technologies
  • Deep experience building or contributing to storage/database internals (query execution, index management, storage engines, transaction/consistency layers)
  • Experience with systems like PostgreSQL, MySQL, RocksDB, SQLite, or comparable infrastructure
  • Understanding of data layout, serialization formats, and access patterns at a deep level
  • Experience shipping and operating storage-layer systems with a focus on correctness under concurrency
  • Understanding of read/write ordering, invalidation hazards, and distributed consistency
  • Knowledge of write-ahead logging, crash recovery, and checkpoint strategies
  • Strong instincts for performance engineering and cost-aware design
  • Ability to reason about cache behavior and identify hot paths
  • Comfort working across large, complex systems (sync engine, formula runtime, client-side rendering, external APIs)
  • Instinct to simplify and rethink architecture when necessary
  • Clear communication and ability to influence across engineering, product, and platform teams

Nice to have

  • Technical leadership

What the JD emphasized

  • 8+ years of industry experience and at least 5 of those working on relevant technologies.
  • Has deep experience building or substantially contributing to the internals of a storage or database system — query execution, index management, storage engines, or transaction/consistency layers in systems like PostgreSQL, MySQL, RocksDB, SQLite, or comparable infrastructure.
  • Understands the tradeoffs in data layout, serialization formats, and access patterns at a level that goes beyond API usage — you've made decisions about how data is actually stored and retrieved under the hood.
  • Has shipped and operated storage-layer systems where correctness under concurrency is non-negotiable — you understand read/write ordering, invalidation hazards, and what "consistent" actually means in a distributed setting.
  • Understands write-ahead logging, crash recovery, and checkpoint strategies — and the tradeoffs between write amplification, recovery time, and storage overhead when designing for durability.
  • Brings strong instincts for performance engineering and cost-aware design: profiling storage operations, reasoning about cache behavior, identifying hot paths, and making principled tradeoffs between performance and operational cost at scale.