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X In most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 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).
- Experience integrating generative AI tools or Large Language Model (LLM) interfaces into workflows.
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.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
About the jobWith your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
Our mission in AI data safety is to reduce critical AI safety risks by transforming production data into continuous safety and quality improvements across Google's generative AI products. Partnering closely with DeepMind, Trust and Safety, and Product Engineering teams, we build and operate foundational, large-scale data pipelines, evaluation frameworks, and observability platforms across Gemini, AI Studio, Search, and Cloud surfaces.
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 .
Responsibilities - Design and build scalable infrastructure, tools, pipelines, workflows and provide high impact data sets to support the rapidly evolving GenAI data ecosystem.
- Design and build scalable systems and platforms to support iterative model quality analysis, continnous model compliance and safety monitoring.
- Apply data enrichment, curation and analysis techniques to improve the compliance and quality of ML models.
- Support client onboarding and engagement on platform.
