Software Engineer III, AI/ML, YouTube Shopping

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Google's YouTube Shopping team is hiring a Software Engineer III in AI/ML for an onsite Zurich role. You will design, deploy, and evaluate ML models for product detection, video classification and timestamp detection, while building scalable data pipelines. You’ll collaborate with cross functional teams to translate model signals into improvements for recommendations and support backend infrastructure for efficient deployment. Requirements include a bachelor’s degree or equivalent, two years of software development or one year with an advanced degree, and at least one year in ML areas such as speech, RL, or ML infrastructure. Preferred: Master’s or PhD, algorithms, experience with DL tools like TensorFlow and TuneLab Studio, and NLP or CV. To apply, highlight projects with measurable gains and end-to-end ML impact.

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Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
• 1 year 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.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• 2 years of experience with data structures and algorithms.
• Experience with deep learning tools (e.g., TensorFlow, TuneLab Studio).
• Experience in the fields of natural language processing, computer vision or multi-modal large language models.
• Experience building and maintaining scalable data pipelines or evaluation frameworks for machine learning systems.

About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; 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. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.

Responsibilities

• Design, implement, and deploy improvements to machine learning models (Product Detection, Video Classification, Timestamp Detection) to enhance accuracy and coverage.
• Build and maintain scalable pipelines for both training data curation and model evaluation. Ensure our models learn from relevant shopping signals by managing high-quality datasets, while developing robust evaluation frameworks to continuously measure performance and analyze edge cases.
• Collaborate with cross-functional teams to ensure new model signals are effectively adopted by downstream systems (Recommendation system, Creator team, etc.)
• Contribute to the back-end infrastructure (orchestration, storage, serving, freshness) to ensure models run efficiently at YouTube scale, directly impacting system latency and cost.
• Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.