# Principal Engineer, Inference Cloud

**Company:** [Cerebras Systems](https://hotfix.jobs/companies/cerebras-systems)
**Location:** Sunnyvale, CA
**Role:** DevOps / SRE
**Experience:** 10+ years
**Skills:** Distributed Systems, Go, C++, Python, Kubernetes, Multi-Region Architecture, Observability, Slo/Sli, Latency Optimization, High-Qps Systems, Ml Inference, Circuit Breaking, Load Shedding, Incident Response, Capacity Planning
**Posted:** 2026-04-15

> Principal Engineer leads Inference Cloud Platform, defining architecture for multi-region, high-QPS AI inference systems. Focuses on reliability, performance optimization, production code, and cross-team technical strategy. Requires 10+ years in distributed systems.

## Job Description

## Responsibilities

- **Problem Definition & Prioritization**: Identify the most important technical problems for the platform, make explicit tradeoff decisions.

- **Platform Direction**: Set long-term technical direction including multi-region topology, failure domains, service boundaries, and system evolution.

- **Reliability & Performance**: Architect active-active systems with rapid failover, graceful degradation (circuit breaking, backpressure, load shedding), SLOs; improve latency, throughput, capacity efficiency, resilience.

- **Code & Design Reviews**: Contribute production code in critical paths, review designs and implementations, make architectural decisions including build-vs-buy.

- **Production Leadership**: Lead on production issues, cross-system bottlenecks; drive observability, incident response, capacity planning, post-incident improvements.

- **Technical Strategy Beyond Your Team**: Drive platform-wide decisions on reliability, API design, capacity planning, deployment strategy; translate product/business requirements into scalable designs.

- **Mentorship**: Raise technical decision-making quality through design feedback, pairing, engineering standards.

## Skills & Qualifications

- 10+ years software engineering experience building/operating large-scale distributed systems or cloud infrastructure.

- Deep expertise in distributed systems architecture: networking, compute orchestration, container platforms, multi-region services.

- Track record of architectural decisions for highly available, latency-sensitive systems at scale.

- Experience optimizing latency, throughput, efficiency in high-QPS systems (TTFT, tail-latency a plus).

- Proficiency in Go, C++, or Python for production code.

- Designing observability/reliability: metrics, logging, tracing, alerting, incident response, SLI/SLO/SLA.

- Influence senior engineers/cross-functional partners via credibility, communication, judgment.

- ML inference infrastructure, model serving, GPU workloads (plus).

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