Senior Staff Software Engineer, Data Quality and ML Infrastructure
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 7 years of experience leading technical project strategy, ML design, and working with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience with design and architecture; and testing/launching software products.
- Experience with distributed storage and processing frameworks (e.g., Spark, Flink, Beam, Presto/Trino, Hadoop) and designing systems that handle petabyte-scale datasets with strict availability and latency requirements.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 5 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
- Experience building data infrastructure for Large Language Models (LLMs) or Foundation Models. Understanding of pre-training vs. fine-tuning data requirements, tokenization at scale, and sequence modeling data structures.
- Understanding of modern table formats (Apache Iceberg, Hudi, Delta Lake) and columnar storage (Parquet, Avro, ORC).
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 manage information at a 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.As a part of Core ML's Applied ML organization, you will accelerate product innovations through machine learning for recommendations and user modeling. You will engage with various product areas and partner with them to help accelerate product innovations through applied research in recommendations and user modeling. You will generalize successful innovations into standardized, maintainable, and production-grade solutions for use by other teams and products.The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're behind Google's groundbreaking innovations, empowering the development of AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Responsibilities
- Design and lead the implementation of a data infrastructure strategy capable of ingesting and processing petabytes of user data, optimizing for high-throughput Tensor Processing Unit (TPU) utilization and balancing storage costs with global availability.
- Partner with applied science and Machine Learning (ML) research leads to translating foundation model requirements into scalable, production-ready infrastructure that enables state-of-the-art model training.
- Build automated frameworks for schema enforcement, anomaly detection, and semantic drift monitoring to ensure data integrity across massive user datasets.
- Define and own strict service level objectives for data freshness and completeness, implementing defensive engineering patterns to shield downstream ML jobs from upstream corruption.
- Act as the primary technical lead while managing a small, agile group of executive engineers, overseeing the tech stack selection, code quality, and the roadmap for high-impact data reprocessing.
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