US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Provide technical leadership in designing, developing, testing, and deploying robust, production-grade ML models and infrastructure.
Drive the architecture, implementation, and maintenance of scalable data pipelines, feature stores, and automated training/evaluation workflows to support continuous model iteration.
Partner closely with Product Managers, Data Scientists, and other engineering teams to translate high-level requirements into concrete technical solutions, influencing the long-term roadmap.
Oversee the monitoring, benchmarking, and optimization of production ML models for latency, throughput, and cost-efficiency without compromising prediction quality.
Minimum qualifications
Bachelor’s degree or equivalent practical experience.
5 years of experience in one of the programming languages: Python or C++.
3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
Preferred qualifications
Master's degree or PhD in Computer Science or related technical field.
5 years of experience with data structures and algorithms.
1 year of experience in a technical leadership role.
Experience developing accessible technologies.
Bachelor’s degree or equivalent practical experience.
5 years of experience in one of the programming languages: Python or C++.
3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
