Staff Software Engineer - Tools & Infrastructure / DevOps
Cerebras · Sunnyvale, CA · Toronto, CAN
CHECKED 7H AGO
InfrastructureremoteStaff
At a glance
Highlights
- Build infrastructure behind the world's largest AI chip, 56x larger than GPUs.
- Help deliver 10x faster AI training and inference unlocking real-time iteration.
- Support workloads for OpenAI partnership deploying 750 megawatts at scale.
What they offer
- Build a breakthrough AI platform beyond the constraints of the GPU
- Publish and open-source cutting-edge AI research
- Work on one of the fastest AI supercomputers in the world
- Job stability with startup vitality
- Simple, non-corporate work culture that respects individual beliefs
Ideal candidate
A seasoned infrastructure leader who loves unblocking developers and driving systemic improvements. You thrive owning large-scale systems and mentoring teams to raise engineering velocity.
Requirements
- 7+ years of professional experience in software engineering, infrastructure engineering, DevOps, developer productivity, or related area
- Deep hands-on experience with CI/CD systems and automated build, test, and deployment infrastructure
- Experience with artifact repositories, software packaging, dependency management, and reproducible builds
- Experience with cloud computing platforms, with AWS preferred, and programmatic infrastructure provisioning
- Strong experience with distributed version control, code review workflows, branching strategies, and repository management
- Strong understanding of Linux/Unix systems, networking fundamentals, and automation scripting or programming
- Experience with containerization and container orchestration, with Kubernetes preferred
- Strong troubleshooting skills for complex distributed systems and infrastructure issues
- Demonstrated experience leading technical initiatives across multiple teams at organizational scale
- Experience operating production systems and participating in on-call, incident response, and postmortems
Lab profile
About Cerebras
Builds wafer-scale AI chips and cloud systems for ultra-fast training and inference.
What they build
- Wafer-scale AI chips
- AI training systems
- Cloud inference service
Known for
- 56x larger chips than GPUs
- 10x faster inference than GPU clouds
- Industry-leading training speed