ML Software Tool Development Engineer

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.

 

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.