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Tier-1 employer and Bangalore metro increase competition, but niche CPU+ML specialization limits applicants.
Specialized CPU architecture and ML kernel expertise make background fit highly industry-specific.
Explicit degree and years plus mandatory C/C++ and CPU/ML optimization skills create stringent filters.
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Lead CPU software-hardware co-design to optimize machine learning workload execution on next-generation QMX architectures.
Perform workload characterization, simulation using QEMU or comparable tools, and identify system bottlenecks to drive architecture and kernel-level performance improvements.
Develop and optimize ML kernels and libraries (e.g., GEMM, convolution) aligning with CPU architecture teams to influence future CPU design features.
Bachelor's degree in Engineering, Computer Science, Information Systems or related, with 4+ years software engineering experience; or Master’s with 3+ years; or PhD with 2+ years.
Minimum 2 years programming experience in C/C++ (mandatory).
Experience with performance profiling, benchmarking, and optimization is required.
Work Experience Required: 2+ years in software engineering or related field as per degree level.
Experienced in CPU architecture concepts, machine learning fundamentals, and systems programming with demonstrated ability in workload characterization and performance optimization.
Skilled in using simulators like QEMU for trace generation and bottleneck analysis with a focus on CPU pipelines, memory hierarchy, and SIMD/vector extensions.
Proficient in developing and optimizing ML kernels and libraries integrated with open-source ML frameworks and low-level optimizations (intrinsics, assembly).