Trainee Google Cloud Machine Learning Engineer Jobs

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Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 10 years of experience with cloud native architecture in a customer-facing or support role.
• Experience with Machine Learning model development or deployment.
• Experience with AI agent orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen), agentic design patterns (e.g., tool-use, multi-agent collaboration), or integrating models into autonomous workflows via advanced API prompting or RAG.
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.

Preferred qualifications:

• Master's degree in Computer Science, Engineering, Mathematics, or a technical field.
• Experience building machine learning solutions and leveraging specific machine learning architectures (e.g., deep learning, LSTM, convolutional networks).
• Experience architecting and developing software or infrastructure for scalable, distributed systems.
• Experience learning and working with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.
• Experience with deep learning frameworks (e.g., PyTorch, TensorFlow, Jax, Ray), AI accelerators (e.g., TPUs, GPUs), model architectures (e.g., encoders, decoders, transformers), or using machine learning APIs.

About the job
When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.
As a Practice Customer Engineer (CE) with a specialty in Cloud AI, you will partner with technical sales teams to differentiate Google Cloud to our customers. You will serve as a technical expert responsible for accelerating technical wins and adoption of complex, specialized workloads. You will leverage your deep expertise in our most strategic product areas, in partnership with Platform CEs, to be writing code to develop prototypes, proofs-of-concept, and demos to sell new, highly specialized solutions to customers. You will solve AI-centered customer challenges and provide a critical feedback loop to influence product development.
You will have excellent organizational, communication, and presentation skills, engaging with customers to understand their business and technical requirements, and persuasively present practical and useful solutions on Google Cloud. You will blend sales expertise, market knowledge, and technical engagement to prove the value of the Google Cloud portfolio.
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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $152000 - $221000 (USD) 42.86% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities

• Drive the technical win for complex workloads within Cloud AI to ensure rapid and successful adoption, primarily supporting the sales cycle from technical evaluation through customer ramp.
• Combine sales strategies and direct development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
• Provide deep technical consultation to customers, acting as a technical advisor and building lasting customer relationships.
• Leverage learnings from customer engagements to contribute to reusable solutions and assets with the Go-To-Market team.
• Work within Product and Engineering management systems to document, prioritize and drive resolution of customer feature requests and issues.

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