Member of Technical Staff, Data Platform , Agi

Amazon Amazon · Big Tech · San Francisco, CA · Software Development

Backend engineer responsible for building and operating core services that ingest, process, and distribute large-scale, multi-modal datasets to internal tools and data pipelines for an AGI research lab. Focuses on designing backend architecture, defining operational standards, and ensuring production health, performance, and observability.

What you'd actually do

  1. Be highly productive in the codebase and drive the team’s engineering velocity.
  2. Design and evolve backend architecture and interfaces for core services.
  3. Define and own standards for production health, performance, and observability.
  4. Collaborate directly with Research, Human Feedback, Product Engineering, and other teams to understand workflows and define requirements.
  5. Write technical RFCs to communicate design decisions and tradeoffs across teams.

Skills

Required

  • 8+ years of professional software engineering experience
  • Experience building and operating backend services for fast-evolving, user-facing products
  • Experience designing and evolving APIs that prioritize ergonomics and maintainability
  • Demonstrated history of independently driving ambiguous, open-ended projects

Nice to have

  • Experience operating at staff-level, or with significant cross-team technical influence
  • Experience building high-throughput services that support complex query patterns
  • Experience with TypeScript and/or Python backend development and toolchain
  • Experience designing and operating services on AWS
  • Experience working in startup or research environments with rapidly evolving requirements

What the JD emphasized

  • backend architecture
  • operational standards
  • production health
  • performance
  • observability
  • large-scale
  • multi-modal datasets
  • backend services
  • fast-evolving
  • rapid experimentation
  • operational rigor
  • reliable services

Other signals

  • research tooling
  • data ingestion
  • large-scale datasets
  • multi-modal datasets
  • backend services
  • operational reliability