Practice Specialist, Machine Learning and Infrastructure, Google Cloud

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Minimum qualifications:

• Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
  
• 5 years of experience in machine learning algorithms, statistical analysis, data mining, and model evaluation.
  
• Experience with large language models, multimodal models, and other generative AI techniques, including their architecture, training methodologies, and fine-tuning.
  
• Experience in Python, PyTorch, and Jupyter/Colab notebooks.
• Experience with open-source ML tools (e.g., Hugging Face, Weights and Biases, and other relevant libraries).
  

Preferred qualifications:

• Experience training and fine-tuning large models (e.g., image, language, etc.) using accelerators (i.e., GPUs, TPUs).
  
• Experience working with cloud-based ML platforms (e.g. Google Cloud Vertex Platform).
  
• Experience with performance profiling tools (e.g., TensorFlow Profiler, PyTorch Profiler, TensorBoard).
  
• Experience with ML performance benchmarks, distributed training, and optimizing performance versus cost trade-offs.
  
• Proficiency in libraries and frameworks (e.g., Hugging Face Transformers, CUDA, PyTorch/XLA (TPUs)).
  

About the job

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

In this role, you will guide customers with platform architecture, migration strategy, and analyze cost and performance benchmarks to help them train and serve Machine Learning models at scale. You will work closely with cross-functional AI teams, Product and Engineering, Infrastructure, and Kubernetes specialists to remove roadblocks and shape the future solutions for customers.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Guide customers on integrating Google Cloud AI accelerators (i.e., GPUs, TPUs) into their overall cloud strategy, recommending migration paths, integration strategies, and architectures that optimize performance and cost.
  
• Demonstrate the power and differentiation of Google Cloud's AI accelerators through proof-of-concept projects.
  
• Work closely with customers to optimize their machine learning models for performance, scalability, and cost-efficiency on Google Cloud infrastructure.
  
• Create and deliver technical content (e.g., best practices, tutorials, code samples, presentations) to enable customers and internal teams to leverage Google Cloud AI infrastructure effectively.
  
• Travel to customer locations and represent Google Cloud at conferences, meetups, and other industry events to share knowledge, network with peers, and stay current on the latest trends.