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AI Workload & Stress Tool Development Design and develop scalable stress, performance, and validation frameworks for MAIA AI accelerator platforms. Build workload generation infrastructure capable of exercising compute, memory, interconnect, networking, storage, and system-level resources. Develop reusable stress tools using PyTorch, Triton, Python, C++, and custom MAIA SDKs. Create synthetic and production-inspired workloads that model training and inference behaviors observed in large-scale AI deployments. Build automated infrastructure for workload deployment, orchestration, telemetry collection, and result analysis. Develop and optimize kernels targeting custom AI accelerators. Analyze execution behavior across the hardware-software stack and identify bottlenecks impacting utilization and performance. Collaborate with compiler and runtime teams to improve workload efficiency and hardware utilization. Develop tooling that integrates with MAIA compiler pipelines, SDKs, runtime environments, and performance analysis tools. Build automation around model compilation, kernel validation, regression testing, and workload portability. Design workload suites for platform bring-up, qualification, and reliability testing. Build comprehensive regression infrastructure supporting silicon, firmware, system software, and platform releases. Develop automated validation tools capable of identifying correctness, performance, thermal, power, and stability issues. Enable platform readiness through scalable validation methodologies and continuous regression testing. Develop benchmarking methodologies and performance dashboards. Developer Productivity & Automation Improve developer productivity through automation, CI/CD integration, diagnostics, and debugging infrastructure. Build reusable tooling for workload generation, failure triage, telemetry analysis, and reporting. Develop dashboards and automated workflows for large-scale validation environments. Doctorate in Electrical Engineering, Computer Engineering, Computer Science, or related field AND 1+ year(s) technical engineering experience OR Master's Degree in Electrical Engineering, Computer Engineering, Computer Science, or related field AND 4+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Computer Science, or related field AND 5+ years technical engineering experience OR equivalent experience. 4+ years of experience with programming skills in C/C++ and Python, with experience designing and developing production-quality software systems, frameworks, runtimes, or infrastructure. 4+ years of experience developing, debugging, or optimizing AI workloads for GPUs, AI accelerators, or HPC systems, including experience with PyTorch or comparable AI frameworks and compute-intensive kernels. 4+ years of experience with accelerator architecture and performance optimization, including compute engines, memory hierarchy, interconnects, runtime systems, performance profiling, bottleneck analysis, and debugging across hardware/software boundaries. These requirements include but are not limited to the following specialized security screenings: Experience with custom AI accelerator SDKs, compiler ecosystems, kernel generation frameworks, or code-generation pipelines, including technologies such as LLVM, MLIR, or Triton. Experience developing or optimizing AI training and inference workloads, including LLMs and other large-scale AI models. Experience with collective communication libraries, high-performance networking, distributed AI systems, or performance characterization of large-scale AI clusters. Experience with CI/CD systems, containerized environments, automated testing, or cloud-scale validation infrastructure. Experience working with silicon bring-up or post-silicon validation teams.
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