We're looking for a founding engineer to own Airweave's data and infrastructure layer, the systems that make our distributed search and data pipelines scalable, reliable and observable.
At Airweave, you'll build and operate the platform that thousands of AI agents depend on. That means distributed sync pipelines pulling data from dozens of sources, vector databases powering LLM search, and the orchestration layer that keeps it all running. You'll work closely with the product team, but your focus is on the foundation: making sure data flows reliably at scale, LLM inference stays fast, and the whole system holds up under real production load.
This is early-stage infrastructure work. The architecture is still being shaped, and your decisions will define how we scale.
What you'll work on
Design and scale distributed data pipelines that sync hundreds of millions of documents from dozens sources into advanced search indexes
Build and improve Temporal workflows for parallel sync orchestration: retries, backpressure, and failure recovery across workers
Own our Kubernetes deployments with Helm charts: autoscaling, and resource management for bursty search, sync and LLM workloads
Scale PostgreSQL for high-throughput; connection pooling, read replicas, partitioning (we ask a lot from this database)
Orchestrate and optimize LLM inference pipelines: batching, caching, provider failover
Build monitoring and alerting with Prometheus, Grafana, and custom instrumentation for cluster health
Infrastructure as code for the base with Terraform
You might be a fit if
You've built or operated data pipelines at scale: ETL, event processing, streaming, or sync infrastructure
You're comfortable with Kubernetes, Terraform, and infrastructure as code
You've scaled databases and understand the tradeoffs (pooling, replication, sharding)
You have experience with distributed systems: workflow orchestration, message queues, eventual consistency
You're interested in LLM infrastructure: embeddings, vector search, inference optimization
You like building reliable systems and have opinions about observability
You're drawn to early-stage environments where you own the whole problem
Bonus points:
Experience with Temporal, Airflow, or similar workflow engines
Background in scaling search (Elastic, Qdrant, Pinecone, Weaviate)
Familiarity with LLM inference
What we offer
Customers including one of the world's leading AI labs
Competitive salary ($120K–$160K) with meaningful equity (0.25%–1.00%)
Health, dental, and vision coverage
Work in-person in San Francisco with a highly-skilled, technical team
Direct impact on architecture and infrastructure decisions from the first week
About Airweave
Open-source context retrieval for AI agents across workspaces. It connects to productivity tools, email, document stores, or any private data source and transforms their contents into searchable knowledge bases for agents.
