Software QA, Lead - Compiler Testing

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Bangalore, Bengaluru, Karnataka
Artificial Intelligence • Machine Learning • Software
The Role

At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.

We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution.  Ready to come find your playground? Together, we can help shape the endless possibilities of AI. 

Location:

Working onsite at our Bengaluru, Karnataka headquarters 3-5 days per week, Hybrid.

Software QA Lead – AI Compiler Testing

Job Description:

We are seeking a highly motivated Software QA Lead to join our AI Compiler team. In this role, you will focus on testing the compiler for AI workloads and ensuring the delivery of high-quality products. Your work will involve writing and maintaining PyTorch workloads, developing unit and feature tests, and driving the resolution of issues. You will collaborate closely with the compiler development team to triage test failures, debug issues, and maintain the integrity of the test infrastructure.

What You will Do:

• Design, implement, and maintain test suites for AI compilers with a focus on PyTorch workloads.

• Develop, automate and execute unit tests, feature tests, and performance tests to validate compiler functionality and performance.

• Work with partners delivering QA solutions.

• Hire, Mentor and train a team on compiler validation.

What You Will Bring:

• Bachelor’s degree in Computer Science, Engineering, or a related field.

• 10+ years of experience in software QA, testing, or a related role.

• Strong experience with AI/ML frameworks, particularly PyTorch.

• Familiarity with compilers, compiler testing, and debugging.

• Proficiency in Python for writing tests and automating workloads.

• Knowledge of CI/CD tools and automated testing frameworks.

• Excellent problem-solving skills and attention to detail.

• Strong communication and collaboration skills for working closely with the development team.

Preferred Qualifications:

• Familiarity with LLVM, MLIR or other AI compiler infrastructure.

• Experience with performance testing and benchmarking.

• Familiarity with other deep learning frameworks such as PyTorch.

Equal Opportunity Employment Policy

d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.

d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.

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The Company
HQ: Santa Clara, CA
102 Employees
On-site Workplace

What We Do

d-Matrix is building a new way of doing datacenter AI inferencing using in-memory computing (IMC) techniques with chiplet level scale-out interconnects. Founded in 2019, d-Matrix has attacked the physics of memory-compute integration using innovative circuit techniques, ML tools, software and algorithms; solving the memory-compute integration problem, which is the final frontier in AI compute efficiency.

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