We are seeking an experienced AI Software Stack Test Lead to define, drive, and execute validation strategy for a next-generation AI computational storage platform. This role owns end-to-end verification of the AI software stack, including neural network operators, compiler-generated workloads, runtime execution, memory management, scheduling systems, and model-level performance.

The ideal candidate combines strong software validation expertise with deep knowledge of AI/ML execution pipelines and will lead validation efforts across compiler, runtime, kernel, and hardware teams to ensure correctness, scalability, reliability, and performance.

Key Responsibilities

Validation Strategy & Technical Leadership

- Own and define the end-to-end validation strategy for the AI software stack.
- Establish validation methodologies, coverage metrics, quality gates, and release-readiness criteria.
- Lead validation efforts across operator libraries, runtime systems, execution frameworks, and AI workloads.
- Mentor engineers and drive best practices for automation, performance validation, and debugging.

Functional Validation
- Validate neural network operators and compute kernels
- Validate graph-level execution and end-to-end model inference workflows.
- Verify numerical correctness against reference frameworks such as PyTorch or TensorFlow.
- Ensure behavioral consistency across software releases and hardware revisions.

Runtime & Execution Validation
- Validate memory allocation and movement across host and device environments.
- Execute stress, concurrency, and multi-device validation scenarios.

Performance & Scalability Validation
- Design and maintain benchmarking frameworks for:




- Operator-level performance
- Model-level performance
- Latency and throughput
- Time-to-first-token/result
- Multi-device scaling efficiency Qualifications

Required Qualifications
- 10+ years of experience in AI software validation, systems software validation, performance engineering, or related areas.
- Strong C++ software development expertise.
- Strong Object-Oriented Programming fundamentals.
- Advanced Python programming for automation and test infrastructure.
- Deep understanding of:
- AI/ML execution pipelines
- Neural network operators
- Deep learning frameworks (PyTorch preferred)
- Runtime architectures
- Scheduling systems
- Parallel execution models
- Memory hierarchy and DMA concepts

- Experience validating large-scale AI workloads and inference systems.
- Proven experience building automated test and benchmarking frameworks.
- Robust debugging and root-cause analysis skills.

Preferred Qualifications
- Experience with MLIR, XLA, StableHLO, TVM, ONNX Runtime, TensorRT, or similar compiler/runtime stacks.
- Experience validating GPU, NPU, FPGA, or custom accelerator platforms.
- Hands-on experience with profiling tools such as: VTune, Nsight Systems, Nsight Compute, perf, gprof
- Experience with distributed inference and multi-device execution.
- Familiarity with LLM, multimodal, and generative AI workloads.
- Experience leading software quality and validation teams.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Staff Test Engineer (Bengaluru)
🏢 Western Digital
📍 Bengaluru