Senior Software Engineer, Google Cloud AI

1 Month ago • 5-8 Years • Artificial Intelligence

Job Summary

Job Description

As a Senior Software Engineer at Google Cloud AI in Warsaw, Poland, you'll manage project priorities and deliverables, designing, developing, testing, deploying, maintaining, and enhancing software solutions. You will contribute to existing documentation, triage system issues, and collaborate with peers through code reviews. The role involves designing and implementing solutions in specialized machine learning areas, leveraging infrastructure and demonstrating expertise in chosen fields. The Google Cloud AI Research team tackles AI challenges across various industries, pushing the state-of-the-art in AI and sharing findings with the broader research community. You'll work on projects critical to Google’s needs with opportunities to switch teams.
Must have:
  • 5+ years software development experience
  • Experience with data structures/algorithms
  • 3+ years experience testing/launching software
  • Software design and architecture experience
  • Machine learning or AI knowledge
Good to have:
  • MS/PhD in CS, AI, or related field
  • Deep learning, LLMs, or speech recognition experience
  • Experience developing accessible technologies
  • Technical leadership experience

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.

Preferred qualifications:

  • MS or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field.
  • 3 years of experience with deep learning, large language models, or speech recognition.
  • Experience developing accessible technologies.
  • Experience in a technical leadership role.
  • Knowledge or experience with, machine learning or AI.

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.

In this role, you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.

Responsibilities

  • Write and test product or system development code.
  • 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.
  • 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,)
  • Design and implement solutions in one or more specialized Machine Learning areas, leverage infrastructure, and demonstrate expertise in chosen fields.

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