Software Engineer Iii, AI Answers Quality

Google Google · Big Tech · Mountain View, CA +2

Software Engineer III role focused on improving AI Answers Quality within Google Search. The role involves developing code for product/system development, participating in design reviews, running live experiments, and triaging/debugging issues. The primary goal is to provide nuanced and diverse answers to user queries, leveraging question-answering technologies at scale. Experience in information retrieval, search quality, ranking systems, and machine learning is preferred.

What you'd actually do

  1. Write product or system development code.
  2. Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
  3. Improve measurements, run live experiments, ensure launches continue to be effective and protect against regressions.
  4. Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  5. 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.

Skills

Required

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience with software development in Python or C++.
  • 1 year of experience testing, maintaining, or launching software products.

Nice to have

  • Master’s degree or PhD in Computer Science or related technical fields.
  • 2 years of experience with data structures and algorithms.
  • Experience in information retrieval, search quality, and ranking systems.
  • Experience with machine learning.

What the JD emphasized

  • AI Answers Quality
  • nuanced answers
  • different types of answers
  • ambiguous questions
  • bold new experiences
  • question-answering technologies at scale
  • information retrieval
  • search quality
  • ranking systems
  • machine learning

Other signals

  • AI Answers Quality
  • nuanced answers
  • different types of answers
  • ambiguous questions
  • bold new experiences
  • question-answering technologies at scale
  • information retrieval
  • search quality
  • ranking systems
  • machine learning