About the job
In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve engineering issues and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally. In this role, you will design the new intelligence layer of the Search’s growth ecosystem with a new long-term life-cycle model.
Responsibilities
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.
Build systems for training, deploying, and monitoring models.
Scale our ML training infrastructure with TensorFlow and JAX.
Optimize model architectures for high-throughput, low-latency environments, ensuring the ML models never compromise core Search performance.
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.
Build the engine that manages and generates hyper-personalized, multi-turn LLM prompts within the new AI Mode infrastructure.
Qualifications
Minimum
Bachelor’s degree or equivalent practical experience.
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, and 1 year of experience with software design and architecture.
Preferred
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.
Ability to start in Mountain View within 4 weeks of offer accept.