Software Engineer III, Machine Learning Deployment, Silicon

1 Month ago • 2 Years + • Research & Development

Job Summary

Job Description

This role involves deploying and optimizing Machine Learning models for high performance on Google Tensor SoCs. Responsibilities include investigating performance issues, leveraging knowledge of Tensor SoC architecture, ML frameworks (TensorFlow Lite), compiler technologies, and kernel optimizations. The position requires developing optimization solutions (model architecture modifications, custom kernels, framework improvements), building tools and libraries for efficient ML deployment, and collaborating with partners and internal teams. A Bachelor's degree in Computer Science or related field, 2 years of C++ and Python experience, and 2 years of experience in machine learning, compilers, or related areas are required. Preferred qualifications include a Master's or PhD, experience with ML frameworks (TensorFlow, PyTorch), and performance analysis tools.
Must have:
  • Bachelor's degree in CS or related field
  • 2 years C++ and Python experience
  • 2 years ML, compiler, or related experience
  • Deploy and optimize ML models
  • Investigate performance issues
  • Develop optimization solutions
Good to have:
  • Master's/PhD in CS
  • Experience with ML frameworks (TensorFlow, PyTorch)
  • Performance analysis tools experience
  • Hardware accelerator optimization experience
  • Collaboration skills
  • Problem-solving and debugging skills

Job Details


Minimum qualifications:

  • Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
  • 2 years of working experience in C++ and Python.
  • 2 years of experience in one or more of the following areas: machine learning, compilers, performance optimization, computer architecture, embedded systems, or systems programming.

Preferred qualifications:

  • Master's degree or PhD in Computer Science or related technical fields.
  • Experience with ML frameworks (e.g., TensorFlow, PyTorch) and ML model architectures.
  • Experience with performance analysis tools for mobile or embedded systems.
  • Experience optimizing software for specific hardware accelerators (e.g., GPUs, DSPs, TPUs).
  • Proven ability to collaborate effectively across team boundaries.
  • Strong problem-solving and debugging skills.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.

Responsibilities

  • Deploy and optimize Machine Learning models from third-party developers for high performance on Google Tensor SoCs.
  • Investigate root causes of performance issues, leveraging knowledge of Tensor SoC architecture, Machine Learning frameworks (e.g., TensorFlow Lite), compiler technologies, and kernel optimizations.
  • Develop and implement optimization solutions, potentially including model architecture modifications (in collaboration), custom compute kernels, or framework-level improvements.
  • Design, build, and maintain tools, libraries, and benchmarks to improve the efficiency of ML deployment, profiling, and analysis.
  • Collaborate effectively with external partners and internal cross-functional teams to understand requirements and deliver solutions.

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