ML Software Tool Development Engineer

About the role

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.

Responsibilities

• Lead the design and implementation of system-level debugging, validation, and observability platforms.
• Develop automated systems for collecting and analyzing numerical, and execution anomalies.
• Create visualization and analysis tools to enable efficient root-cause investigation.
• Build frameworks for failure classification, regression detection, and anomaly monitoring.
• Extend compilers, runtimes, and programming interfaces to support advanced profiling and instrumentation.
• Improve system bring-up, low-level debug, and validation workflows.
• Partner cross-functionally with compiler, hardware, firmware, runtime, and infrastructure teams.
• Establish best practices for debuggability, reliability, and operational excellence.
• Lead high-impact initiatives.
• Support incident response and drive long-term corrective actions.

Skills & Qualifications

• 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.

Preferred Skills & Qualifications

• 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.