Staff Software Engineer, Shorts ML Infrastructure

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.

In this role, you will have the opportunities to work in a changing environment on the Machine Learning (ML) infrastructure for Shorts-specific requirements and problems, including: novel model training and serving infrastructure; large-scale training data pipelines; low-latency/real-time training data; online learning (e.g., continuous training with streaming injected training data and partial model updates); feature engineering infrastructure, such as high-dimensional features; and training and serving optimizations across a wide range of ML models.

The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Design and implement highly scalable, reliable, and efficient ML infrastructure solutions to support research and production workloads.
• Stay at the forefront of ML infrastructure technologies and research, identifying and evaluating new approaches to improve performance, scalability, and cost-effectiveness.
• Understand YouTube Shorts product initiatives and Shorts technical architecture to allow for translating product initiatives into ML infra initiatives and delivering product impact incrementally, balancing short-term quick wins and longer-term deliveries with good judgment.
• Provide technical leadership and guidance to junior engineers, fostering a culture of collaboration, innovation, and learning.
• Partner with research scientists, ML engineers, and product teams to understand their needs and deliver infrastructure solutions that accelerate their work.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development, with a focus on building and maintaining large-scale distributed systems, and experience with Python and C++.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, and debugging).
• 3 years of experience with machine learning algorithms, data structures, software design patterns, and system architecture.

Preferred qualifications:

• Experience with ML infrastructure tools and platforms (e.g., Kubeflow, MLflow, TFX, Spark, and Flink).
• Experience in Search/Ads/Recommendation ML methodologies.
• Experience in data analysis and data driven decision making to allow defining and implementing ML infra performance and model quality observables, and reasoning of such metrics with concrete training data distribution.
• Experience with hardware acceleration technologies (e.g., GPUs, TPUs) and their application to ML workloads.
• Understanding of MLOps principles and practices, including CI/CD, model monitoring, and experiment tracking.