The opportunity
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.
What you'll do
Design, develop, test, and maintain production software, with: responsibilities spanning testing, continuous development, observability, security, networking, debugging, and productionization.
Raise the effectiveness of senior engineers through design feedback, pairing, and clear technical standards.
Platform Direction. Help shape the technical direction for the Inference: Platform, Kubernetes custom resource definitions, failure domains, service boundaries, and system evolution over time, and own the roadmap for major technical areas.
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.
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.
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.
What they're looking for
- Technical Influence. Partner with ML, Product, Infrastructure, and Cloud: teams to translate product and business requirements into scalable system designs, and drive alignment on shared technical decisions within your domain and adjacent platform surfaces.
- + 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, ideally with Kubernetes.
- Strong track record of making sound architectural decisions for highly: available, latency-sensitive systems at scale.
