Backend Software Engineer — Data Platform & AI Data Products

Together AI Together AI · Data AI · San Francisco, CA · Engineering

Backend Software Engineer focused on building data platform infrastructure and LLM-adjacent data products. The role involves designing and developing backend services for event streams, access layers, and APIs, as well as creating services for prompt categorization, enrichment, and metadata. The engineer will apply AI augmentation mindset to their own development and the systems they build, with a focus on production backend systems, distributed systems, and data modeling.

What you'd actually do

  1. Identify, design, and develop foundational data infrastructure components capable of handling millions or billions of events daily
  2. Analyze and improve the robustness and scalability of existing data processing infrastructure
  3. Partner with product teams to understand functional requirements and deliver solutions that meet business needs
  4. Write clear, well-tested, and maintainable infra-as-code and software for both new and existing systems
  5. Conduct design and code reviews, create developer documentation, and develop testing strategies for robustness and fault tolerance

Skills

Required

  • production backend systems
  • distributed systems
  • APIs
  • data services
  • Go
  • Python
  • Java
  • Rust
  • API design
  • REST
  • gRPC
  • infra-as-code
  • software development
  • design docs
  • implementation
  • testing
  • deployment
  • on-call
  • clean code
  • maintainable code
  • design patterns
  • data modeling
  • SQL
  • Streaming/eventing
  • Kafka
  • PubSub
  • Kinesis
  • Workflow/compute
  • Airflow
  • Spark
  • Flink
  • Trino
  • OLTP/OLAP stores
  • data lakes
  • Postgres
  • warehouse tech
  • AI augmentation mindset
  • coding copilots
  • agentic workflows
  • eval-driven iteration
  • retrieval/knowledge grounding
  • engineering judgment
  • mitigation mechanisms
  • guardrails

Nice to have

  • self-serve platforms
  • control planes
  • templates/golden paths
  • paved roads
  • multi-tenant services
  • LLM/AI product exposure
  • prompt/response telemetry
  • eval datasets
  • embeddings/RAG metadata
  • model/tool traces
  • privacy-safe logging
  • Security and governance
  • least-privilege access
  • auditability
  • data retention
  • PII handling

What the JD emphasized

  • 6+ years building production backend systems
  • Demonstrated ability to own services end-to-end
  • AI augmentation mindset

Other signals

  • building backend services
  • data products
  • LLM-adjacent services
  • prompt categorization/taxonomy
  • enrichment
  • metadata systems
  • telemetry into trusted, usable products