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About the Role
As a Senior Software Development Engineer in Test, you will innovate and execute tests on cutting-edge AI infrastructure, focusing on simplifying large hardware deployments with push-button, single-pane-of-glass observability/monitoring and software capabilities for built-in resiliency. Your goal is to ensure cluster reliability of 99.999% in large deployments.
Responsibilities
- Innovate and execute tests on cutting-edge AI infrastructure.
- Define optimized test strategies and methodologies.
- Adapt to new technologies and bring expertise.
- Build a strong understanding of how to break large distributed systems challenges into smaller, unit-testable components.
- Aim for 100% automated tests for cluster features including high availability, failure scenarios, performance, stress, and security.
- Champion cluster security, reliability for uptime of 99.9999%, and ease of use with observability.
- Test all components of AI clusters, including cluster software (kubernetes, prometheus, grafana) and hardware components (ML wafer scale accelerators, CPU runtime nodes, High speed swarmx interconnect, High speed data transfer of weights through memoryx interconnect).
Requirements
- Bachelor's or Master's degree in engineering in computer science, electrical, AI, data science, or related field.
- 5+ years of experience in testing enterprise software, distributed systems, datacenter hardware and software.
- Strong coding skills in Python, Golang, or C/C++.
- Strong debugging skills for large distributed systems, hardware, and software.
- Experience with debugging tools like pdb, gdb, strace, and network monitors.
- Strong understanding of operating systems internals (memory management, file system, security, performance).
- Strong understanding of datacenter layout and device performance characteristics (Servers, Memory, BIOS, PCIe, networking, storage).
- Experience with cloud technologies like AWS, Kubernetes, and Dockers.
- Understanding and experience of ML model training and inference is a huge plus.
- Understanding of ML hardware accelerators like GPU, custom accelerator ASIC is a huge plus.
Skills
- Python
- Golang
- C/C++
- Kubernetes
- Prometheus
- Grafana
- AWS
- Dockers
- Debugging tools (pdb, gdb, strace)
- Network monitors
- Operating systems internals
- Datacenter layout
- Device performance characteristics
- ML model training
- ML model inference
- ML hardware accelerators (GPU, custom accelerator ASIC)
Location
- Remote
Work Type
- Full-time
Experience Level
- 5+ years
Education Level
- Bachelor's degree
- Master's degree
Benefits
- Job stability with startup vitality
- Simple, non-corporate work culture
- Continuous learning, growth, and support
About the Company
- Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs, delivering industry-leading training and inference speeds.
- Our architecture transforms the user experience of AI applications, unlocking real-time iteration and increasing intelligence.
- We work with leading model labs, global enterprises, and cutting-edge AI-native startups.
- OpenAI has a multi-year partnership with Cerebras to deploy 750 megawatts of scale.
- We are committed to creating an equal and diverse environment.
- We believe inclusive teams build better products and companies.
Equal Opportunity
- Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer.
- We celebrate different backgrounds, perspectives, and skills.
- We believe inclusive teams build better products and companies.
- We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.