AI/DSP Kernel Optimization Engineer

Job Description

We are looking for an AI/DSP Kernel Optimization Engineer to develop and optimize AI inference kernels, analyze application performance, and integrate optimized kernels into AI inference pipelines. The role involves low-level C/C++ optimization, SIMD/vectorization, profiling, debugging, and close collaboration with hardware and software teams to improve overall inference performance.

Responsibilities:

• Develop and optimize AI inference kernels using C/C++.
• Optimize kernels for different data types, including INT8, INT16, BF16, and FP32.
• Perform SIMD/vectorization, intrinsics, memory optimization, and performance tuning.
• Profile and analyze application performance to identify optimization opportunities.
• Debug and resolve functional and performance issues.
• Integrate optimized kernels into the AI inference pipeline.
• Work closely with hardware and software teams to improve overall inference performance.

Requirements

Education:

B.E., B.Tech., M.E., M.Tech., or equivalent.

Technical Skills (Must haves):

• Good knowledge of C/C++ programming.
• Basic understanding of AI/Deep Learning models and inference.
• Experience with DSPs, hardware accelerators, or similar compute platforms.
• Knowledge of SIMD/vectorization, intrinsics, and low-level software optimization.
• Good profiling, debugging, and performance analysis skills.

Need to have (Can be bridged):

• Understanding of computer architecture and memory optimization.

Good to have (Not essential):

• Experience in AI/ML inference optimization.
• Knowledge of different numerical data types such as INT8, INT16, BF16, and FP32.
• Familiarity with performance profiling and benchmarking tools.

Preferred Qualifications (Optional):

• Experience with C7x DSP, HWA-MMA, or similar hardware accelerators.
• Hands-on experience optimizing inference workloads for DSPs or specialized compute platforms.