Senior Software Engineer, AI/ML, Google Cloud

2 Hours ago • 5-8 Years • Artificial Intelligence

Job Summary

Job Description

Google is seeking a Senior Software Engineer with expertise in AI/ML and Google Cloud. This role requires a minimum of 5 years of software development experience, including data structures and algorithms, along with 3 years in testing, maintaining, or launching software products. Candidates need 3 years of experience in areas such as speech/audio processing, reinforcement learning, ML infrastructure, or a related ML field, plus 3 years of experience with ML infrastructure (model deployment, evaluation, optimization, data processing, and debugging). Responsibilities include writing and testing code, collaborating on design and code reviews, contributing to documentation, triaging issues, debugging, and designing and implementing solutions in specialized ML areas. The ideal candidate will leverage ML infrastructure, demonstrate expertise in a chosen field, and contribute to Google's next-generation technologies.
Must have:
  • 5+ years software development experience
  • 3+ years testing/launching software
  • 3+ years experience in Speech/audio, reinforcement learning, or ML infrastructure
  • 3+ years experience with ML infrastructure (deployment, evaluation, etc.)
  • Proficiency in data structures and algorithms
  • Software design and architecture experience
Good to have:
  • Master's or PhD in CS
  • Technical leadership experience
  • Experience developing accessible technologies

Job Details

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 5 years of experience with software development in one or more programming languages, and with data structures/algorithms.
  • 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.
  • 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.

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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