Senior Software Engineer, AI/ML, Google Cloud Compute

Sunnyvale, CAOnsiteFull TimeSenior$174,000–$252,000 /yrPosted 1 month agoVisa sponsorship available

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Required skills

Python
C++
ML Infrastructure
Speech/Audio Technology
Reinforcement Learning
Software Design
Software Architecture
Software Testing
model deployment
model evaluation

Nice to have

Data Structures
Algorithms
Technical Leadership
Accessible Technologies

Role summary

Google is seeking Senior Software Engineers to develop next-generation technologies that impact billions of users globally. This role focuses on AI/ML within Google Cloud Compute, requiring expertise in programming languages like Python or C++, software design, architecture, and testing. The engineer will work on critical projects, contributing to massive-scale information systems and leveraging ML infrastructure for tasks such as model deployment and optimization. The position involves writing and testing code, collaborating with peers, debugging issues, and designing ML solutions. The role offers opportunities for growth and versatility in a fast-paced environment.

Minimum qualifications:

Bachelor’s degree or equivalent practical experience.
5 years of experience programming in Python or C++.
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

Master's degree or PhD in Computer Science or related technical field.
5 years of experience with data structures/algorithms.
1 year of experience in a technical leadership role.
Experience developing accessible technologies.

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 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: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

Write and test product or system development code.
Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency,)
Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.

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