Senior Software Engineer, Inference Cloud

About the role

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 Senior Software Engineer for our Inference Cloud team. This team owns the cloud layer behind our Inference Service, including availability, latency, reliability, and global scale.

This is a hands-on individual contributor role focused on building cloud services and the Kubernetes infrastructure that runs them. You’ll design and own cloud components, improve request routing and service connectivity, work with production databases, and debug issues across the stack. You’ll take projects from design through rollout and ongoing operation, partnering closely with ML, Product, and Infrastructure teams.

Responsibilities

• Kubernetes Infrastructure. Build and improve production Kubernetes environments on Amazon EKS. Own improvements to workload scheduling, autoscaling, cluster upgrades, resource utilization, network policies, and failure recovery.
• Service Networking & Traffic Control. Develop and operate traffic management infrastructure using Envoy and Istio. Improve routing, rate limiting, connection management, timeouts, retries, and failover to keep services responsive under changing demand.
• Databases. Build database-backed services and improve the reliability and performance of their data stores. Troubleshoot query performance, connection pooling, and availability issues, and strengthen backup and recovery practices.
• Reliability & Performance. Identify bottlenecks across applications, Kubernetes, networking, and databases. Improve latency, throughput, and capacity efficiency in high-QPS systems, including during traffic spikes and partial failures.
• Infrastructure & Deployment Automation. Use Terraform, Argo CD, and Kustomize to automate infrastructure provisioning and application delivery. Improve deployment validation, rollback, and operational tooling.
• Production Operations & Collaboration. Build actionable metrics, logs, traces, dashboards, and alerts. Investigate incidents, deliver lasting fixes, and contribute to design reviews, code reviews, and shared engineering practices.

Skills & Qualifications

• 5+ years of software engineering experience, including building and operating production services or cloud infrastructure.
• Strong Kubernetes expertise, including workload lifecycle, networking, service discovery, storage, autoscaling, and troubleshooting cluster and application failures.
• Hands-on experience with Amazon EKS or a comparable managed Kubernetes service.
• Experience configuring and troubleshooting Envoy and Istio, or comparable proxy and service mesh technologies. Solid understanding of HTTP, gRPC, DNS, TLS, and mTLS.
• Experience developing services that use production databases, with practical knowledge of query optimization, indexing, connection management, and database reliability.
• Strong programming skills in Go or another backend or systems language, with the ability to write, test, and maintain production software.
• Experience with infrastructure as code and automated deployment workflows using Terraform, Argo CD, Kustomize, or equivalent tools.
• Experience diagnosing performance and reliability issues using metrics, logs, and tracing.
• Ability to independently deliver projects, explain technical tradeoffs, and collaborate effectively across engineering teams.

Preferred Skills & Qualifications

• AWS networking experience, including VPNs, Transit Gateway (TGW), Direct Connect (DX), VPC routing, and security groups.
• Experience designing distributed systems, including replication, consistency, fault tolerance, and multi-region operation.
• Production programming experience in C++.
• Experience optimizing latency, throughput, and efficiency in high-QPS systems.
• Experience reducing time to first token (TTFT) and tail latency is a strong plus.
• Familiarity with ML inference infrastructure or model serving systems.