Reliability Engineer (AI Infrastructure)

Location: Hybrid in San Mateo or New York, United States

Salary: $240,000–$290,000 annually plus equity

Company

hirify.global provides an AI platform for building, training, and serving specialized models across text, image, embedding, audio, and multimodal workloads.

What you will do

• Define SLOs, error budgets, production-readiness criteria, and on-call expectations.
• Own logging, telemetry, alerting, failure-injection, load-testing, and self-healing automation tooling.
• Identify reliability issues across service boundaries, dependencies, retries, timeouts, and multi-region infrastructure.
• Coordinate production incidents, lead blameless postmortems, and track corrective actions.
• Automate repetitive operational work and reduce on-call toil.
• Partner with cloud infrastructure, inference, training, performance, product, and control-plane teams.

Requirements

• 5+ years working with Linux internals, system performance troubleshooting, and TCP/IP, HTTP, and gRPC networking.
• 5+ years writing production-grade tools or systems code in Python, Go, C++, or Rust.
• Experience operating and debugging Kubernetes, Terraform, and Docker in high-throughput production environments.
• Experience with distributed systems, microservices, high-throughput control planes, or multi-region deployments.
• Knowledge of fault-tolerant design, SLO/SLA management, automated failover, and high-availability architecture.
• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or equivalent practical experience.

Nice to have

• Experience with Prometheus, Grafana, OpenTelemetry, and actionable alerting.
• Exposure to GPUs, inference serving, or distributed training.
• Experience building AI-assisted operations tools, agents, or LLM-based investigation and automation tooling.
• Open-source contributions to infrastructure, systems, or ML-serving projects.
• Startup experience and comfort working pragmatically across teams.

Culture & Benefits

• Work on challenging AI infrastructure problems, including low-latency inference and scalable model serving.
• High ownership and direct impact in a fast-growing product company.
• Collaboration with experienced engineers and AI researchers.
• Equity included in the compensation package.