Senior Software Engineer, Ai/ml, Search Growth

Google Google · Big Tech · Mountain View, CA +1

Senior Software Engineer, AI/ML, Search Growth at Google. This role focuses on designing and deploying advanced AI/ML models, building associated training and monitoring systems, and optimizing model performance for low-latency environments within Google Search's growth ecosystem. The position involves leveraging multi-modal embeddings and LLM-generated user profiles, designing A/B tests, and building infrastructure for hyper-personalized LLM prompts in a new AI Mode.

What you'd actually do

  1. Design and deploy advanced models (e.g., Contextual Bandits, Transformers, Sequence Modeling) to optimize promo inventory by leveraging on multi-headed objective functions that balance growth goals against user annoyance costs.
  2. Build systems for training, deploying, and monitoring models. Scale our ML training infrastructure with TensorFlow and JAX.
  3. Optimize model architectures for high-throughput, low-latency environments, ensuring the ML models never compromise core Search performance.
  4. Drive model performance by testing and ingesting novel signals (including multi-modal embeddings and Large Language Model (LLM)-generated user profiles), designing and executing A/B tests to measure ML-driven feature effectiveness, and iterating quickly based on findings.
  5. Build the engine that manages and generates hyper-personalized, multi-turn LLM prompts within the new AI Mode infrastructure.

Skills

Required

  • 5 years of experience with one general-purpose systems language (e.g., Java, Kotlin, C++, or Go)
  • 4 years of experience building and maintaining production-grade, latency-sensitive backend or ML systems
  • 3 years of experience in Deep Learning and ML System Design
  • 3 years of experience testing, maintaining, or launching software products
  • 1 year of experience with software design and architecture

Nice to have

  • Master's degree or PhD in Computer Science or related technical field
  • Experience in Recommendation Systems (Ranking/Prediction), NLP, Reinforcement Learning, or Information Retrieval
  • Ability to deep-dive into datasets to identify the next high-Return on Investment (ROI) area for technical investment
  • Ability to balance long-term platform health and engineering with short-term business and growth goals, to address engineering problems at Search scale
  • Ability to drive technical designs from concept to launch while successfully challenging the status quo

What the JD emphasized

  • latency-sensitive backend or ML systems
  • Deep Learning and ML System Design
  • software design and architecture
  • Recommendation Systems (Ranking/Prediction), NLP, Reinforcement Learning, or Information Retrieval

Other signals

  • design and deploy advanced models
  • build systems for training, deploying, and monitoring models
  • optimize model architectures for high-throughput, low-latency environments
  • drive model performance by testing and ingesting novel signals
  • build the engine that manages and generates hyper-personalized, multi-turn LLM prompts