Cerebras Systems
ML Systems Integration Engineer
Headquarters/Sunnyvale Office
Sponsorship not specifiedDetected 8 days ago
PythonDistributed SystemsLinuxMachine LearningComplianceElectrical EngineeringCommunicationProblem Solving
About the role
- 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.
- OpenAI recently announced a multi-year partnership https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Responsibilities
- Build automation frameworks and internal tooling that improve system validation and debugging workflows.
- Develop software used to test, validate, and stress distributed hardware systems during development and production cycles.
- Collaborate closely with hardware engineers to isolate and resolve system integration issues.
- Improve system observability by building tools that surface failures quickly and accelerate debugging.
- Support validation and qualification of new hardware generations as systems move toward production readiness.
- Ability to work effectively across multiple engineering teams and collaborate in highly technical environments.
- Experience building automation frameworks, internal tooling, or test infrastructure
- People who are serious about software make their own hardware.
Nice to have
- Preferred Skills & Qualifications
Skills
- Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups.
Company info
- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source their cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non-corporate work culture that respects individual beliefs.
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This listing is sourced directly from Cerebras Systems's careers page and normalized into a canonical job model.