Sr. Staff, Software Machine Learning Test Engineer

d-Matrix; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

d-Matrix is seeking a Sr. Staff Software Machine Learning Test Engineer to help productize the SW stack for the d-Matrix AI compute engine.

You will join the Software QA team and own test automation development for d-Matrix components, and maintain and improve the test infrastructure for next-generation AI hardware. Using GitLab CI/CD and the Pytest framework, you will scale the test framework to deliver qualified software stacks and hardware products, performing model accuracy benchmarking,

Qualifications

• Experience building test automation for ML software and hardware qualification.
• Proficient with Pytest and CI/CD pipelines.
• Strong collaboration with ML core kernel, compiler, runtime, and hardware teams.
• Ability to design scalable test infrastructure.
• Excellent debugging and problem-solving skills.

Responsibilities

• Develop and maintain test automation for d-Matrix components.
• Scale test framework for end-to-end qualification.
• Leverage GitLab CI/CD and Pytest for automation.
• Contribute to ML/AI benchmarking and robustness tests.
• Collaborate with cross-functional teams.

Skills

Python

Pytest

Test automation

CI/CD

Machine Learning QA

Tools

GitLab CI/CD

Pytest Framework

Job description

What you will do

Role: Sr. Staff, Software Machine Learning Test Engineer

The role requires you to be part of the team that helps productize the SW stack for the d-Matrix AI compute engine. As part of the Software QA team, you will be responsible for test automation development for d-Matrix components and products, and for maintaining and improving the test infrastructure for next‑generation AI hardware. You will build and scale the test framework to deliver highly qualified software stacks and hardware products, leveraging the d-Matrix GitLab CI/CD infrastructure and Pytest framework to achieve component and end‑to‑end qualification. You will develop large language model accuracy, benchmarking, and robustness testing using your own developed automation, and work with global ML core kernel teams, compiler teams, runtime teams, hardware architecture experts, and machine learning model researchers during the qualification process. You may also contribute to new ML/AI technologies.