ML Systems Integration Engineer
Cerebras builds wafer-scale AI computing systems and a cloud inference platform for training, fine-tuning, and serving AI models.
About Cerebras Systems, Inc.
Cerebras Systems is an AI-infrastructure company founded in 2015. It sells rack-scale wafer-scale computing systems and provides cloud-based, API-accessible AI inference alongside on-premises deployments.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will bring up AI hardware systems and supporting software infrastructure. You will debug hardware and software interactions, investigate failures, identify root causes, and build automation and test frameworks. You will improve observability, validate new hardware generations, and improve debugging and testing workflows.
Requirements
- Programming skills in Python or C++
- Debugging and problem-solving skills
- Knowledge of operating systems, processes, threads, memory management, concurrency, and IPC
- Experience with Linux development environments
- Knowledge of computer architecture and hardware-software interactions
- Analytical root-cause analysis
- Communication skills
- Experience building automation frameworks, internal tooling, or test infrastructure
- Knowledge of distributed systems
- Knowledge of networking
- Performance analysis
- Telemetry
- Log analysis
- Infrastructure reliability engineering
Responsibilities
- Participate in bring-up of next-generation AI hardware systems and supporting software infrastructure
- Debug system-level hardware and software issues
- Investigate bring-up failures and identify root causes using logs, telemetry, and diagnostic tools
- Build automation frameworks and internal tooling for validation and debugging
- Develop software to test, validate, and stress distributed hardware systems
- Collaborate with hardware engineers to resolve system integration issues
- Build observability tools that surface failures and accelerate debugging
- Reproduce, triage, and diagnose issues during early hardware deployment
- Support validation and qualification of new hardware generations
- Improve debugging, testing, and automation workflows