Senior AI Engineer

3 weeks ago

Waterloo, Ontario, Canada Huawei Technologies Canada Co., Ltd. Full time
Senior AI Engineer

We are seeking a highly skilled Senior AI Engineer to join our team at Huawei Technologies Canada Co., Ltd. As a key member of our AI engineering team, you will be responsible for developing and maintaining real-time and historical performance monitoring tools for AI inference workloads.

Key Responsibilities:

  • Develop and maintain performance monitoring tools for AI inference workloads, including profiling tools for various AI model types.
  • Analyze and classify inference workloads based on characteristics like profile, decode, pre/post-processing overheads, and computational complexity.
  • Develop performance models that consider the systematic factors of AI inference, including model size, architecture, and compute resource characteristics.
  • Optimize inference workloads across various hardware resources by reducing latency, minimizing memory overhead, and improving throughput.
  • Lead efforts in creating benchmarks for different types of inference tasks.
  • Conduct benchmarking and performance comparisons across various hardware platforms to identify bottlenecks and optimization opportunities.
  • Work closely with AI research, software engineering, and DevOps teams to improve the end-to-end AI inference pipeline.

Requirements:

  • Ph.D. or Master's degree in Computer Science, Electrical Engineering, Machine Learning, or related field.
  • Minimum 5+ years of experience in AI/ML engineering with a focus on inference performance, workload analysis, and system optimization.
  • Extensive experience with AI frameworks and model optimization techniques.
  • Proficient with profiling tools and workload analysis for diverse AI models and applications.
  • Expertise in optimizing small models, large language models, VLMs, and multimodal models for inference.
  • Strong programming skills in Python, C++, CUDA, and experience with low-level hardware performance tuning.
  • Familiarity with performance modeling methodologies and frameworks for predicting inference workload performance under varying conditions.
  • Proven expertise in data parallelism, model parallelism, pipeline parallelism, and other distributed systems for performance improvements at scale.

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