Founding Software Engineer, Data Infrastructure

We're building the connective tissue between AI and user data. Today's AI agents are brilliant but mostly blind: they can reason, plan, and execute, but they can't see what's happening across users' work. Every app is a silo. Every database is an island. We're changing that with a focused technical team and great customers, including one of the world's leading AI labs.Airweave turns scattered information into searchable and learned intelligence. We give agents the context they need by ensuring the right information is available when decisions are made.The next wave of software will connect dots across systems and work alongside people naturally. That won't come from a bigger model. It will come from the hard work of connecting intelligence to the data in our work and world. We're building the infrastructure to make that possible.The RoleWe'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 onDesign and scale distributed data pipelines that sync hundreds of millions of documents from dozens of sources into advanced search indexesBuild and improve Temporal workflows for parallel sync orchestration: retries, backpressure, and failure recovery across workersOwn our Kubernetes deployments with Helm charts: autoscaling, and resource management for bursty search, sync, and LLM workloadsScale PostgreSQL for high-throughput; connection pooling, read replicas, and partitioning (we ask a lot from this database)Manage vector database (Vespa) infrastructure: sharding, replication, backup strategies for large-scale agentic searchOrchestrate and optimize LLM inference pipelines: batching, caching, provider failoverBuild monitoring and alerting with Prometheus, Grafana, and custom instrumentation for cluster healthInfrastructure as code for the base with TerraformYou might be a fit ifYou've built or operated data pipelines at scale: ETL, event processing, streaming, or sync infrastructureYou're comfortable with Kubernetes, Terraform, and infrastructure as codeYou've scaled databases and understand the tradeoffs (pooling, replication, sharding)You have experience with distributed systems: workflow orchestration, message queues, and eventual consistencyYou're interested in LLM infrastructure: embeddings, vector search, inference optimizationYou like building reliable systems and have opinions about observabilityYou're drawn to early-stage environments where you own the whole problemNice to haveExperience with Temporal, Airflow, or similar workflow enginesBackground in scaling search (Elastic, Qdrant, Pinecone, Weaviate)Familiarity with LLM inferenceWhat we offerCustomers including one of the world's leading AI labsCompetitive salary (€80K–€100K) with meaningful equity (0.25%–0.75%)Health, dental, and vision coverageWork in-person in San Francisco with a highly-skilled, technical teamDirect impact on architecture and infrastructure decisions from the first weekHiring ProcessFounders call (45 min) – learn about our vision, share your story.Paid work trial (3 days, on-site) – work on a real problem with the team. You'll get a feel for the codebase, how we collaborate, and whether this is the right fit for both sides. We cover your travel expenses.Offer – we decide fast (usually within 24 hours).Our ValuesThe systems we build ... (truncated, view full listing at source)