# ML Software Tool Development Engineer

**Company:** [Cerebras Systems](https://hotfix.jobs/companies/cerebras-systems)
**Location:** Unspecified
**Role:** ML Engineering
**Skills:** C++, Python, compilers, runtime systems, profiling, instrumentation, Distributed Training, Machine Learning, hpc, hardware interfaces
**Posted:** 2026-02-17

> Build system-level debugging, validation, observability, and anomaly-analysis tooling for Cerebras’s AI hardware and software stack. The role requires strong C++ and Python skills, experience debugging complex hardware/software systems, and familiarity with compilers, runtimes, or high-performance computing.

## Job Description

## 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 debugging, 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.

## Requirements
- 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.

## Nice-to-haves
- 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.

## Benefits
- Opportunity to build a breakthrough AI platform beyond the constraints of GPUs.
- Opportunities to publish and open-source cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Job stability with startup vitality.
- A simple, non-corporate work culture that respects individual beliefs.

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