A frontier data provider powering foundational AI labs.
Staff Platform Engineer, Applied AI
You'll build and harden the distributed backbone that moves petabytes of training data through Kafka pipelines, Redis caches, and ElasticSearch clusters—then orchestrate it all on Kubernetes at the exacting standards AI research demands. One morning you're tracing a latency spike in a NodeJS ingestion service; the next, you're pairing with an ML team to shave hours off an experiment cycle. This is hands-on leadership: you ship, you mentor, you set the bar for how reliable infrastructure enables breakthrough science.
What they're looking for
- 6+ years designing and running production distributed systems, with deep fluency in Kubernetes and at least two of {Kafka, Redis, ElasticSearch}
- Strong Python and TypeScript—enough to refactor critical services, review code sharply, and debug across the stack
- Track record of leading platform decisions in ambiguity: you choose trade-offs, document them, and get buy-in without hiding behind process
- Experience supporting GPU-heavy or large-scale data workloads; you understand where infrastructure bottlenecks kill research velocity
- Genuinely on-site in San Francisco—this team builds fastest shoulder-to-shoulder