Senior Principal Engineer, Inference Cloud Platform

Cerebras; Sunnyvale (CA); On-site; USD 260,000 - 350,000; Full time

Job summary

Cerebras Systems is seeking a Principal Engineer to own and shape the Inference Cloud Platform, the cloud layer behind our Inference Service. You will own multi-region topology, latency, availability, and scale while writing production code on critical paths.

The role demands framing ambiguous problems, setting long-term direction, and driving execution across teams. You will lead on reliability, performance, and operational rigor, collaborating with ML, Product and Infrastructure groups to

Qualifications

• 10+ years of experience in software engineering, with substantial individual contributor experience building and operating large-scale distributed systems or cloud infrastructure.
• Deep expertise in distributed systems architecture in cloud environments, including networking, compute orchestration, container platforms, and multi-region production services.
• Strong track record of making sound architectural decisions for highly available, latency-sensitive systems at scale, demonstrated through systems you built directly.
• Experience optimizing latency, throughput, and efficiency in high-QPS systems. Experience with TTFT and tail-latency reduction is a strong plus.
• Strong proficiency in backend or systems languages such as Go, C++, or Python, with the expectation that you can contribute production code directly.
• Experience designing observability and reliability practices, including metrics, logging, tracing, alerting, incident response, and SLI/SLO/SLA-driven operations.
• Ability to influence senior engineers, technical leads, and cross-functional partners through technical credibility, communication, and judgment.
• Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads is a plus.

Responsibilities

• Problem Definition & Prioritization.Identifythe most important technical problems for the platform, often before there's a clear ask. Make explicit tradeoff decisions about what the platform will andwon'tsupport, with reasoning that holds up under scrutiny from senior engineering leadership.
• Platform Direction.Set the long-term technical direction for the Inference Cloud Platform, including multi-region topology, failure domains, service boundaries, and system evolution over time.
• Reliability & Performance.Architect active-active systems with rapid failover and graceful degradation (circuit breaking, backpressure, load shedding) with clear SLOs. Drive improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand.
• Code & Design Reviews.Contribute production code in critical paths, reviewdesignsand implementations, and make architectural decisions including build-vs-buy tradeoffs with long-term operational consequences.
• Production Leadership.Lead on the hardest production issues and cross-system bottlenecks. Drive observability, incident response, capacity planning, and post-incident improvement with a high standard for operational rigor.
• Technical Strategy Beyond Your Team.Drive platform-wide decisions across adjacent teams on reliability, API design, capacity planning, and deployment strategy through strong technical judgment. Translate product and business requirements into scalable system designs and drive alignment on shared infrastructure decisions.
• Mentorship.Raise the quality of technical decision-making across teams through design feedback, pairing, and clear engineering standards.

Skills

Distributed systems

Backend languages

Cloud architecture

Observability

Leadership

ML inference infra

Job description

Cerebras Systems is seeking a Principal Engineer to own and shape the Inference Cloud Platform, the cloud layer behind our Inference Service. You will own multi-region topology, latency, availability, and scale while writing production code on critical paths.

The role demands framing ambiguous problems, setting long-term direction, and driving execution across teams. You will lead on reliability, performance, and operational rigor, collaborating with ML, Product and Infrastructure groups to