Staff Software Engineer, Inference Cloud
Staff engineer owning the architecture of Cerebras' Inference Cloud Platform — designing multi-region traffic routing, load balancing, admission control, and reliability/SLO systems for high-QPS AI inference, while writing production code in Go, C++, or Python and leading technical direction.
- Compensation
- Not disclosed
- Location
- Sunnyvale, United States
- Work setup
- Onsite
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Software engineering
- Posted
Skills
Must have
- 8+ years of software engineering experience with substantial IC 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
- Track record of sound architectural decisions for highly available, latency-sensitive systems at scale
- Experience optimizing latency, throughput, and efficiency in high-QPS systems
- Strong proficiency in backend or systems languages such as Go, C++, or Python, with ability to contribute production code directly
- Experience designing observability and reliability practices: metrics, logging, tracing, alerting, incident response, SLO-driven operations
- Ability to influence senior engineers and cross-functional partners through technical credibility, communication, and judgment
Nice to have
- Experience with TTFT and tail-latency reduction
- Experience with ML inference infrastructure, model serving systems, or GPU-accelerated workloads
Full description
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.
This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.
Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
About the Role
We're hiring a Staff Engineer to own major areas of the architecture of our Inference Cloud Platform. This team owns the cloud layer behind our Inference Service, with responsibility for availability, latency, reliability, and global scale.
This is a hands-on individual contributor role for an engineer who wants to work on the hardest distributed systems problems in the stack: multi-region traffic architecture, graceful degradation under bursty AI workloads, performance at high QPS, and the operating model for a platform that has to stay fast and available under load. You'll write code, lead key architectural decisions in your domain, debug production issues, and help shape technical direction across adjacent teams.
If you're interested in building the next-generation architecture of a globally distributed inference platform, we'd like to talk.
Responsibilities
- Platform Direction. Help shape the technical direction for the Inference Cloud Platform, including multi-region topology, failure domains, service boundaries, and system evolution over time, and own the roadmap for major technical areas.
- Core Cloud Systems. Design and build critical platform components such as service discovery, request routing, load balancing, caching, batching, and traffic management for AI inference workloads.
- Reliability & Performance. Architect active-active systems with rapid failover, graceful degradation, and clear SLOs. Drive system-level improvements in latency, throughput, capacity efficiency, and resilience under unpredictable demand.
- Traffic Control & Service Tiers. Define platform mechanisms for admission control, quota management, rate limiting, and differentiated quality of service across workload types and customer tiers.
- Execution on Critical Paths. Write and review production code in the most important parts of the platform. Make high-consequence architectural decisions within your area and set the technical bar through design reviews, code reviews, and sound engineering judgment.