Requirements

What you've done before

• Built large-scale data or backend products. You have solid experience working with systems where volume, latency, reliability, and cost all matter.
• Shipped products from concept to production. You’ve owned scoping, architecture, implementation, release, measurement, and iteration—not just one layer of the solution.
• Designed distributed systems. You can reason clearly about throughput, failure modes, data flow, scalability, and operational tradeoffs.
• Built LLM-powered or agentic features in production. You understand the practical differences between models and can balance capability against latency and cost.
• Worked autonomously on ambiguous problems. You know how to gather requirements, ask useful questions, and turn incomplete context into forward motion.
• Communicated clearly across teams. You can explain technical tradeoffs to engineers and non-engineers, give direct feedback, and collaborate without creating unnecessary process.
• Worked in a fast-moving product environment. You’re comfortable learning quickly, shipping incrementally, and changing direction when the evidence does.

Bonus points if you’ve worked with multimodal embeddings, vector databases, semantic search, ranking algorithms, model deployment, self-hosted models, or GPU infrastructure. Curiosity about the creator economy helps too, but we’ll get you up to speed.

Our stack

• AWS and GCP, with Pulumi for infrastructure as code
• Python, TypeScript, and Node.js
• PySpark on AWS EMR
• Airflow
• Milvus Vector DB through Zilliz
• Elasticsearch
• LLM batch APIs
• Apache Iceberg
• SageMaker, DynamoDB, S3, Glue, Kinesis, Lambda, ECS, and Aurora
• Slack, GitHub, Linear, Notion, and Cursor