Senior Software Engineer, Machine Learning, Payments

1 Month ago • 5-8 Years • Research & Development

Job Summary

Job Description

This Senior Software Engineer role at Google's Payments team in Bengaluru requires 5+ years of software development experience, proficiency in data structures/algorithms, and expertise in GenAI techniques (LLMs, Multi-Modal, Large Vision Models). Responsibilities involve writing and testing code, collaborating on design and code reviews, contributing to documentation, debugging systems, and designing/implementing GenAI solutions leveraging ML infrastructure. The ideal candidate will have experience with model deployment, evaluation, and optimization, along with strong problem-solving and collaboration skills. The role contributes to building secure and efficient payment systems for Google's global users.
Must have:
  • 5+ years software development experience
  • Data structures/algorithms expertise
  • GenAI techniques (LLMs, etc.) experience
  • ML infrastructure experience (model deployment, etc.)
  • Code writing, testing, and review
  • Debugging and problem-solving skills
Good to have:
  • Master's/PhD in CS
  • Technical leadership experience
  • Experience with 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 state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (e.g., language modeling, computer vision).
  • 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.

Whether it is paying online with Autofill, using tap and pay in stores, or using the Google Pay app, the Payments team at Google is focused on making payments simple, seamless, and secure. In addition to consumer payment technologies, the Payments team also powers the money movement between Google and its consumers and businesses.

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 GenAI solutions, leverage ML infrastructure, and evaluate tradeoffs between different techniques and their application domains.

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