2 days ago
Zürich, SwitzerlandStaff+
Responsibilities
- Execute quality iterations on reference agents to identify model or agent-framework improvements.
- Source or facilitate the creation of datasets for evaluation and training to improve model performance for Cloud customers.
- Design agentic systems and context-engineering algorithms using existing models, while identifying new training and evaluation objectives when needed.
- Define and implement metrics aligned with business problems.
- Prototype and iterate on solutions with customers, product management, and business development.
- Lead engineering of the ML quality feedback loop through hypothesis-driven quality iteration, experimental systems optimization, and data engineering for analysis.
Requirements
- Bachelor’s degree or equivalent practical experience.
- Eight years of experience working with Large Language Models and building agents.
- Understanding of tokens, context, Retrieval-Augmented Generation, function calling, core ML concepts, training algorithms, and evaluation practices.
- Understanding of data, basic statistical analysis, and general data science principles.
- Familiarity with defining technical metrics, implementing evaluation frameworks, designing experiments, analyzing results, performing root-cause analyses, formulating hypotheses, and conducting ablation or live experiments.
- Proficiency in Python.
Tech Stack
Google CloudPython
Categories
AI Research
About Google
Google builds consumer and enterprise software and services including Search, Android, YouTube, Chrome, Maps, Gmail, and Google Cloud. Its business model centers on digital advertising and paid cloud, software, and hardware offerings (e.g., Pixel and Nest) for consumers, developers, and organizations. Founded in 1998 and headquartered in Mountain View, California, Google operates globally as a subsidiary of Alphabet Inc.