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Customer Solutions Engineer

$127,000–$183,000 + equityAustin, TXFull-time

Customer Solutions Engineer designing and delivering enterprise Generative AI solutions across the Google Cloud stack.

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At a glance

Posted base pay

$127K–$183KBelow the median

Midpoint $155K · median $190K

Compare pay across all Google postings

The work

Builds what the customer runs

Balance Mostly building

Project stages

  1. Pre-sales
  2. Proof of concept
  3. Implementation
  4. Operations (not covered)
Coding
Writes production code
Builds for
Both
Level
Senior
Role
Individual contributor

What they ask for

Experience

  • 3+ yearsJob offered or Customer Solutions Engineer-related occupation with a bachelor's degree in a listed related field
  • 1+ yearsJob offered or Customer Solutions Engineer-related occupation with a master's degree in a listed related field, as an alternative to the bachelor's degree and 3-year path
  • 1+ yearsApplied machine learning using Vertex AI; AI/ML frameworks such as TensorFlow or PyTorch; large-scale model training and fine-tuning with accelerators including Vertex AI; ML model performance and cost optimization; and systems design for data/ML pipelines, training, and serving

Background

  • Solutions and customer engineering
  • Implementation and data engineering

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Conditions

Location
Austin, TX
Employment type
Full-time
Pay
Pay details

The US base salary range for this full-time position is $127,000 - $183,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google.

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The job description from Google

About the job

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.

The US base salary range for this full-time position is $127,000 - $183,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google.

Responsibilities

  • Advise customers on their business objectives to design and architect comprehensive Generative AI-driven solutions using the full Google Cloud stack.
  • Demonstrate Google Cloud's value by developing application prototypes, tuning models for optimal performance, and troubleshooting complex technical issues.
  • Engineer end-to-end solutions by scoping requirements, writing production-quality code, and ensuring compliance with engineering and data policies.
  • Create reusable technical assets, including scripts, templates, and reference architectures, to accelerate adoption for both customers and internal teams.
  • Collaborate cross-functionally with product and strategy teams to influence platform direction by advocating for enterprise customer requirements. Engage in account business planning to identify new opportunities and coordinate with leadership to deliver technical enablement for field and partner organizations.

Qualifications

Minimum qualifications:

  • Bachelor’s degree in Computer Science, Information Systems, Computer Engineering, or a related field and 3 years of experience in the job offered or in a Customer Solutions Engineer-related occupation.
  • Alternatively, will accept a Master’s degree in Computer Science, Information Systems, Computer Engineering, or a related field and 1 year of experience in the job offered or in a Customer Solutions Engineer-related occupation.
  • Position requires 1 year of experience in the following: Applied machine learning techniques (including deep learning, natural language processing, computer vision, or pattern recognition) using VertexAI; Using AI/ML frameworks such as TensorFlow or PyTorch; Training and fine-tuning large scale models with accelerators including VertexAI; Performance and cost optimization for machine learning models; and Systems design for data and ML pipelines, and ML training and serving approaches.

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