Google

Google

Senior Software Engineer, AI/ML, Geo and Gemini App

New York, NY, USA · Senior

Sponsorship not specified$174k-$253kDetected 8 hours ago
C++Full-Stack DevelopmentData StructuresMachine LearningTensorFlowPyTorchData AnalysisNLPLLMsRAGA/B TestingUI DesignResearchExperimental DesignLeadership

About the role

  • - Bachelor's degree or equivalent practical experience.
  • - 5 years of experience developing C++ backend infrastructure components within distributed systems.

Responsibilities

  • Build context engineering pipelines, agentic workflows with tool usage, and robust evaluation frameworks.

Requirements

  • Bachelor's degree or equivalent practical experience.
  • 5 years of experience developing C++ backend infrastructure components within distributed systems.
  • 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
  • Experience with Large Language Models (LLMs) and Generative AI concepts (e.g., prompt engineering, retrieval-augmented generation (RAG)).
  • Experience with model quality optimization using methods such as loss analysis or quality hillclimbing.

Nice to have

  • 5 years of experience with data structures and algorithms.
  • 1 year of experience in a technical leadership role.
  • Experience working in fast-paced, startup-like environments with rapidly changing priorities.
  • Experience with common ML frameworks (e.g., TensorFlow, JAX, PyTorch).
  • Familiarity with user metrics logging and A/B testing for software products.
  • Our products need to handle information at massive scale, and extend well beyond web search.
  • 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.

Compensation

  • US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits

This listing is sourced directly from Google's careers page and normalized into a canonical job model.