Member of Technical Staff (AI Infrastructure Reliability)

Join Fireworks AI, a leader in open model AI infrastructure. As a Member of Technical Staff in Reliability Engineering, you will ensure the platform runs dependably as it grows. You will define reliability standards, own the reliability toolchain, manage incident response, and partner across the organization. This role requires a strong background in systems engineering, software development, and cloud-native operations.

• Define and drive the adoption of reliability standards, including SLOs, error budgets, and production readiness criteria.
• Own the reliability toolchain, including logging and telemetry pipelines, alerting standards, failure injection, load testing, and self-healing automation.
• Coordinate live production issues, run blameless postmortems, and track follow-ups to completion, while reducing toil through automation.

• Cloud-native operations: Operating and debugging Kubernetes, Terraform, and Docker in high-throughput production
• Reliability fundamentals: Fault-tolerant design, SLO/SLA management, automated failover, high-availability architecture
• Breadth over comfort: Willingness to dig into unfamiliar parts of the stack when a problem crosses boundaries
• Systems fundamentals: 5+ years with Linux internals, system performance troubleshooting, and networking fundamentals (TCP/IP, HTTP, gRPC)
• Education: Bachelor's or Master's in Computer Science, Computer Engineering, or equivalent practical experience
• Distributed systems: High-throughput control planes, microservices, or multi-region setups
• Software engineering: 5+ years in Python, Go, C++, or Rust, writing production-grade tools and systems code
• Influence without authority: You can get other teams to adopt a standard through credibility and useful tooling rather than mandate
• Observability tooling: Prometheus, Grafana, OpenTelemetry, and alerting people actually act on
• GPU and ML infrastructure exposure: GPUs, inference serving, or distributed training
• AI-assisted operations: Building agents or LLM-based tooling for investigation, triage, or automation
• Open source background: Contributions to infrastructure, systems, or ML serving projects
• Startup agility: Comfortable where pragmatism and teamwork matter more than process