Job description

As a Customer Engineer (CE), you will partner with technical Sales teams to differentiate Google Cloud to our customers. You will serve as the customer’s primary technical partner and trusted advisor, engaging in technical-led conversations to understand their business issues. You will troubleshoot technical questions and roadblocks, engage in proofs of concepts and demos, and use your expertise to architect cross-pillar cloud solutions that solve these business issues. You will drive the technical win and define the delivery and consumption plans. You will use your presentation skills to engage with technical and business leaders, and persuasively present practical and useful solutions on Google Cloud. You will have excellent technical, communication and organizational skills. You will focus on identifying, pursuing, and winning new business workloads and driving pen testing within existing ones. You will have a breadth of technical expertise, spanning infrastructure modernization, application modernization, data analytics and more. You will blend sales expertise, market knowledge and direct technical engagement to show 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 . • Accelerate customer time-to-value on the largest AI Infrastructure and High Performance Computing (HPC) workloads in Google Public Sector. • Build a trusted advisory relationship with customer architects, engineering leadership, and research teams. Identify customer priorities, technical objections and design strategies focused on Google AI Infrastructure and HPC ecosystem to deliver business value and resolve blockers. • Provide domain expertise around hardware accelerators (GPU/TPU), prevailing ML Frameworks (PyTorch, Keras, JAX), and model building techniques. • Make recommendations on Graphics Processing Unit/Tensor Processing Unit (GPU/TPU) hardware, framework selection, benchmarks, and model building required to successfully implement a complete solution. • Manage the holistic research engineering relationship with customers by collaborating with specialists, product management, technical teams, and more. • Bachelor's degree or equivalent practical experience. • 10 years of experience with cloud native architecture in a customer-facing or support role. • Experience engaging with, or presenting to, technical stakeholders or executive leaders. • Experience with machine learning (ML) model development and deployment. • Active US Government Top Secret/Sensitive Compartmentalized Information (TS/SCI) security clearance with polygraph. • Ability to travel up to 20% of the time. 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 engaging with, or presenting to, technical stakeholders or executive leaders. • Experience with machine learning (ML) model development and deployment. • Active US Government Top Secret/Sensitive Compartmentalized Information (TS/SCI) security clearance with polygraph. • Ability to travel up to 20% of the time. Preferred qualifications: • Experience with prevailing ML development frameworks (e.g., Keras, PyTorch, Tensorflow, JAX). • Experience with both GPU and TPU based infrastructure. • Familiarity with prevailing AI related tooling (Slurm, vLLM, Ray, Vertex, K8s, etc.). • Familiarity across the AI software development life cycle (data processing, model building, training, evaluation, deployment). • Ability to deliver results and work cross-functionally to position and orchestrate a solution consisting of multiple products.

Requirements

Must-haves

Learn more about benefits at Google .
Accelerate customer time-to-value on the largest AI Infrastructure and High Performance Computing (HPC) workloads in Google Public Sector.
Provide domain expertise around hardware accelerators (GPU/TPU), prevailing ML Frameworks (PyTorch, Keras, JAX), and model building techniques.
Make recommendations on Graphics Processing Unit/Tensor Processing Unit (GPU/TPU) hardware, framework selection, benchmarks, and model building required to successfully implement a complete solution.
Manage the holistic research engineering relationship with customers by collaborating with specialists, product management, technical teams, and more.
Bachelor's degree or equivalent practical experience.
10 years of experience with cloud native architecture in a customer-facing or support role.
Experience engaging with, or presenting to, technical stakeholders or executive leaders.
Experience with machine learning (ML) model development and deployment.
Active US Government Top Secret/Sensitive Compartmentalized Information (TS/SCI) security clearance with polygraph.
Ability to travel up to 20% of the time.
Bachelor's degree or equivalent practical experience.
10 years of experience with cloud native architecture in a customer-facing or support role.
Experience engaging with, or presenting to, technical stakeholders or executive leaders.
Experience with machine learning (ML) model development and deployment.
Active US Government Top Secret/Sensitive Compartmentalized Information (TS/SCI) security clearance with polygraph.
Ability to travel up to 20% of the time.

Nice-to-haves

US: $152000 - $221000 (USD) + 42.86% bonus target + equity + benefits
Experience with prevailing ML development frameworks (e.g., Keras, PyTorch, Tensorflow, JAX).
Experience with both GPU and TPU based infrastructure.
Familiarity with prevailing AI related tooling (Slurm, vLLM, Ray, Vertex, K8s, etc.).
Familiarity across the AI software development life cycle (data processing, model building, training, evaluation, deployment).
Ability to deliver results and work cross-functionally to position and orchestrate a solution consisting of multiple products.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learning

Platform: google
Effort: medium