Staff Software Engineer, AI/ML, Cloud IAM, Dataset and Model Training

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.

The Cloud IAM team builds foundational authorization and policy intelligence systems securing Google Cloud Platform for enterprise customers, workloads, and AI agents. You will build core ML models and data pipelines transforming Cloud IAM from a static access control system into an adaptive, risk-aware intelligence engine. Combining dataset engineering and model training expertise with cloud security and reliability focus, you will design scalable data pipelines, train compact risk-scoring models, and integrate explainable ML predictions with deterministic security guardrails. Your systems will evaluate access risk in real time, recommend least-privilege policies, detect risky permission combinations, and help security administrators automate access decisions safely and efficiently.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google (https://www.google.com/about/careers/applications/benefits/).

Minimum qualifications:

- Bachelor’s degree or equivalent practical experience.

- 8 years of experience in software development.

- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.

- 5 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.

- 5 years of experience leading ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

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.

- 3 years of experience in a technical leadership role leading project teams and setting technical direction.

- 8 years of experience training, fine-tuning, and evaluating machine learning and deep learning models in Python (e.g., PyTorch, JAX, scikit-learn, GBDTs/XGBoost, or Transformers).

- Experience with dataset curation, feature engineering, synthetic data generation, and building reproducible ML training and evaluation pipelines at scale.

- Experience developing ranking, retrieval, classification, or risk/anomaly scoring models for production systems.

- Design and scale training data pipelines using access telemetry, policy graphs, and synthetic data with rigorous de-noising.

- Train and optimize ML models for Intelligent IAM, focusing on access-risk scoring, anomaly detection, and least-privilege recommendations.

- Build explainability and calibration layers to provide clear reasoning for risk scores, routing uncertain cases to human review or static rules.

- Partner with security engineers to identify IAM edge cases, ensuring models learn subtle privilege boundaries without over-granting access.

- Establish evaluation benchmarks and online monitoring to prevent model drift, and optimize models for low-latency Google Cloud serving.