ML Software Tool Development Engineer

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.

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  Lead the design and implementation of system-level debugging, validation, and observability platforms.

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  Develop automated systems for collecting and analyzing numerical, and execution anomalies.

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  Create visualization and analysis tools to enable efficient root-cause investigation.

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  Build frameworks for failure classification, regression detection, and anomaly monitoring.

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  Extend compilers, runtimes, and programming interfaces to support advanced profiling and instrumentation.

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  Improve system bring-up, low-level debug, and validation workflows.

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  Partner cross-functionally with compiler, hardware, firmware, runtime, and infrastructure teams.

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  Establish best practices for debuggability, reliability, and operational excellence.

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  Lead high-impact initiatives.

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  Support incident response and drive long-term corrective actions.

• Strong proficiency in C++ and Python, with a track record of building reliable, high-performance systems and tooling.

• Demonstrated experience debugging complex hardware/software systems and driving issues to root cause.

• Experience analyzing system-level data structures, execution graphs, or dependency networks for diagnostics and validation.

• Proven ability to design and build intuitive visualization and analysis tools for complex technical data.

• Experience with compiler internals, custom hardware interfaces, or low-level protocol design.

• Strong written and verbal communication skills, with the ability to explain technical concepts to diverse stakeholders.

• Ability to work independently and lead complex technical projects end-to-end.

• Familiarity with machine learning training and inference pipelines, especially distributed training and large-model scaling.

• Prior work on high-performance clusters, HPC systems, or custom hardware/software co-design.

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.

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