Location: Hybrid-Santa Clara, CA headquarters, 3-5 days/week onsite
Team: Kernels | Reports to: Engineering Manager, Kernels
Cross-functional interface: QA / Test Engineering
About the RoleWe are looking for a Staff Software Engineer, Test & Validation - AI Kernels to embed within our Kernels team and own the quality engineering function for our AI compute software stack. You will design and build the test infrastructure, validation frameworks, and automated verification pipelines that ensure correctness, performance, and reliability of software kernels running on next-generation AI hardware. You will serve as the primary quality interface between the Kernels team and the broader QA organization, driving alignment on test strategy, coverage, and release readiness.
This is a high-impact, deeply technical role that requires both strong software engineering skills and a rigorous quality mindset. You will work alongside compiler engineers, ML software engineers, and hardware architects while partnering closely with QA leads to define standards and share best practices across the company.
What You Will DoTest Infrastructure & Automation- Design, build, and maintain scalable automated test frameworks for kernel validation across simulation, emulation, and silicon targets.
- Develop correctness and performance regression suites covering ML operators (GEMMs, convolutions, BLAS, SIMD ops, softmax, layer norm, pooling, etc.).
- Implement testing pipelines integrated into CI/CD workflows, enabling rapid feedback on kernel changes.
- Build tooling to automate comparison of kernel outputs against reference implementations (e.g., CPU-based golden references, PyTorch/TensorFlow baselines).
Kernel & Hardware Validation- Develop test coverage for software kernels targeting specialized hardware including AI accelerators, DSPs, FPGAs, and SIMD vector processors (e.g., Tensilica).
- Validate numerical precision, edge cases, and boundary conditions for ML operators across data types and hardware configurations.
- Partner with hardware teams (mixed signal, DSP, CPU) to validate hardware-software co-design assumptions and catch integration issues early.
- Drive validation of compiler-generated code paths (MLIR, LLVM, TVM, etc.) through structured test methodologies.
QA Partnership & Process- Act as the primary liaison between the Kernels engineering team and the QA organization, aligning on test plans, coverage criteria, and release qualification gates.
- Participate in QA planning ceremonies; represent Kernels team needs and surface quality risks early.
- Contribute to and help maintain shared QA infrastructure, test standards, and reporting dashboards used across engineering.
- Drive root cause analysis for test failures and escaped defects; work with Kernels engineers to close gaps.
Technical Leadership- Set quality standards and best practices for the Kernels team; mentor engineers on testability design and defensive coding.
- Contribute to design reviews with a quality lens, identifying areas that require additional validation before tape-out or release.
- Influence test strategy across the full software stack: from unit and integration tests to system-level validation on target hardware.
What You Will BringMinimum Qualifications- BSc in Computer Engineering, Computer Science, Math, Physics, or a related field with 6+ years of industry experience; or MS with 4+ years of experience; or PhD with 2+ years of experience, with at least 2 years focused on test engineering, SDET, or quality engineering roles.
- Strong understanding of computer architecture, data structures, and machine learning fundamentals, sufficient to reason about correctness and performance of ML kernels.
- Proficient in C/C++ and Python; experience writing production-quality test code in these languages in Linux environments.
- Experience writing automated tests for algorithms targeting specialized hardware such as GPUs, DSPs, FPGAs, or AI accelerators (e.g., using CUDA or equivalent).
- Familiarity with ML operators commonly used in production workloads: GEMMs, convolutions, BLAS, SIMD operations, softmax, layer normalization, pooling, and similar.
- Experience building or working within CI/CD pipelines and automated regression systems.
- Track record of owning quality outcomes, not just executing tests but defining strategy, coverage, and release readiness criteria.
- Strong cross-functional communication skills; comfortable working across hardware and software teams and representing quality concerns to non-QA stakeholders.
Preferred Qualifications- Experience embedded in a hardware-adjacent software team (AI accelerator company, cloud compute, or similar) rather than a purely software QA role.
- Hands-on experience with ML frameworks such as PyTorch or TensorFlow, including the ability to write reference implementations for operator validation.
- Familiarity with ML compiler toolchains (MLIR, LLVM, TVM, Glow, etc.) and how to test compiler-generated artifacts.
- Experience with embedded SIMD vector processors such as Tensilica.
- Prior startup or small-team experience; comfort operating with high ownership and limited process overhead.
- Experience defining and implementing formal test plans and coordinating release qualification with a QA organization.
Why This RoleYou will have a direct impact on the correctness and quality of AI compute software that ships in next-generation hardware products. As the first Staff SDET on the Kernels team, you will build quality infrastructure from the ground up and establish practices that will scale with the organization. You will work with some of the best compiler and kernel engineers in the industry, with the unique vantage point of sitting at the intersection of hardware and software quality.
