About the RoleYou'll architect, build, and maintain the distributed systems, APIs, and backend infrastructure that power world-class AI research and deployment. Your work will enable rapid experimentation, robust data flows, and scalable compute. This is a hands-on role for someone who thrives in small, high-agency teams.
Responsibilities- Design and implement scalable backend services, data pipelines, and APIs
- Build infrastructure for high-throughput, low-latency AI/ML workflows
- Set up and manage databases (SQL/NoSQL), including schema design, replication, indexing, and performance optimization
- Optimize for performance, reliability, and security at every layer
- Implement observability best practices (logging, tracing, monitoring, performance tuning)
- Work closely with research and infra teams to support experimental velocity
- Contribute to core engineering culture, including code reviews and mentorship
Requirements- 3+ years building distributed systems or high-availability backends (preferably in fast-moving or high-stakes environments)
- Strong proficiency in Python and at least one systems language (e.g., Go, Rust, C++)
- Experience with cloud platforms, containerization, and orchestration (AWS/GCP/Azure, Docker, Kubernetes)
- Database setup, management, and optimization experience (SQL/NoSQL)
- Relentless focus on code quality, correctness, and performance
- Comfortable leveraging modern AI coding tools (Cursor, Claude Code, Codex, etc.) to increase productivity & code output while maintaining high code quality, maintainability, and structure
Bonus - Agentic workflows / applied AI integration- Experience supporting or integrating agentic AI workflows into backend systems (e.g., LangChain, LangGraph, Semantic Kernel, Haystack, or custom frameworks)
- Familiarity with persistence/checkpointing, streaming pipelines, and vector/knowledge store integrations
