Software Engineer, AI and Data Protection

Google•Kirkland, WA

1d•$207,000 - $300,000

About The Position

The AI and Data Protection team builds and operates the high-throughput, high-availability content analysis services that power Google's AI and data protection capabilities. Our technology is fundamental to Cloud Sensitive Data Protection (Cloud SDP), Gmail and Google Drive's Data Loss Prevention (DLP) systems, Gemini training data protection, inference protection with Model Armor, and a wide range of other Google products. We develop the infrastructure, APIs, algorithms, and models that provide fast, scalable classification and redaction of sensitive information and AI content risks. As a Software Engineer, you will contribute to high-impact initiatives to create and enhance our classification models, improve the reliability, efficiency, and performance of our AI security and content analysis engines. You will innovate and deliver end-to-end solutions that safeguard data for Google, as well as Google Cloud customers. You will provide technical guidance to other engineers, and work closely with partner teams, cross-functionally and with customers.

Requirements

Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience with distributed processing and large-scale data processing.

Nice To Haves

Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
8 years of experience with data structures and algorithms.
3 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.

Responsibilities

Develop server code in C++, Java or Go for content analysis with high availability at scale.
Develop novel algorithms for detecting AI security risks, classifying structured and unstructured sensitive information.
Train, evaluate, and productionize ML/Natural Language Processing (NLP) models for token tagging and payload classification tasks.
Develop data mining pipelines, synthetic data generation pipelines, and build datasets for training and evaluation.
Partner with research groups on developing innovative ML/NLP solutions.