Software Engineer III, ML, TPU Efficiency, YouTube

• Profile ML workloads, identify compute and memory bandwidth bottlenecks, and optimize accelerator utilization to maximize compute efficiency.
• Explore, implement, and productionize algorithmic efficiency techniques, including low-precision quantization, knowledge distillation, parameter sharing, and attention optimizations.
• Optimize auxiliary serving and distributed data pipelines, including data ingestion, feature transformation, embedding lookups, and memory caching to support real-time training and inference.
• Partner closely with ML model developers and researchers to co-design hardware-friendly model architectures and deploy universal efficiency libraries.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 2 years of experience programming in C++ or Python.
• 2 years of experience with software design and architecture.
• 2 years of experience testing, and launching software products.
• Experience with ML model optimization.
• Experience with ML frameworks such as TensorFlow, JAX, and PyTorch, or ML compilers (e.g., accelerated linear algebra (XLA)).

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• Experience developing accessible technologies.
• Experience with debugging correctness and performance issues at all levels of the ML software stack.
• Experience with ML compilers and their internals, experience writing compiler optimization passes.
• Familiarity with accelerator hardware architectures (TPUs/GPUs).
  

• Bachelor's degree or equivalent practical experience.
• 2 years of experience programming in C++ or Python.
• 2 years of experience with software design and architecture.
• 2 years of experience testing, and launching software products.
• Experience with ML model optimization.
• Experience with ML frameworks such as TensorFlow, JAX, and PyTorch, or ML compilers (e.g., accelerated linear algebra (XLA)).