Software Engineer, Machine Learning, TPU Workload Optimization

• Act as ML software engineer leading the onboarding for stable stack, and optimization for training and serving solutions on the latest TPU NPIs.
  
• Possess extensive technical knowledge of firmware design, kernel development, and server system integration, with the ability to influence the ecosystem.
  
• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
  
• Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
• Lead the server software design, development, and implementation on a scalable and high-quality Cloud Server Program, overseeing the project from conception to launch.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  
• 1 year of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 1 year of experience with training (e.g., Megatron-LM, DeepSpeed) and inference techniques (e.g., TensorRT-LLM, vLLM, SGLang).

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
  
• Experience in optimizing machine learning models for large scale training/inference workloads.
  
• Experience in different large scale ML optimizations techniques for improving latency and throughput.
  
• Experience with accelerators (TPUs or GPUs), or HPC.

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  
• 1 year of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 1 year of experience with training (e.g., Megatron-LM, DeepSpeed) and inference techniques (e.g., TensorRT-LLM, vLLM, SGLang).