Staff Software Engineer, AAI Research in Zürich

Google Research Zurich in Switzerland is seeking a Staff Software Engineer for its AAI Research team within Google Cloud.

This role focuses on developing next-generation agentic systems and driving the discovery of advanced AI capabilities.

Candidates must possess at least eight years of experience with Large Language Models (LLMs) and agent building, alongside proficiency in Python.

Key responsibilities include executing quality iterations on reference agents, designing new agentic systems, and defining metrics aligned with business problems.

The ideal candidate demonstrates a solid understanding of core ML concepts, RAG, function calling, and statistical analysis.

Engineers will collaborate closely with product teams to deploy high-performance AI systems that benefit customers across various industries.

This position requires versatility, leadership qualities, and the ability to innovate across the full stack while contributing to scientific impact.

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Project Description Bachelor's degree or equivalent practical experience.8 years of experience working with Large Language Models (LLMs) and building agents.

Preferred qualifications: Solid understanding of essential concepts such as tokens, context, Retrieval-Augmented Generation (RAG), and function calling.

Solid understanding of core ML concepts, training algorithms, and best practices for evaluation.

Strong understanding of data and familiarity with basic statistical analysis concepts (e. g., variance, p-value, bias) and general data science principles.

Familiarity with running quality interactions, involving understanding business goals, defining technical metrics, implementing evaluation frameworks, designing experiments, analyzing results, performing RCAs, formulating hypotheses, and conducting ablation or live experiments.

Proficiency in Python.

About the job Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another.

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 Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve.

You will anticipate our customer needs and be empowered to act like an owner, take action and innovate.

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 agent benchmark engineering team drives the discovery and expansion of next-generation agent capabilities, empowering customers to deploy high-performance AI systems.

You will deliver robust, production-ready agentic systems by leading the engineering of the ML quality feedback loop including: hypothesis-driven quality iteration, experimental systems optimization, data engineering for analysis.

The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries.

We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community.

We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.

Responsibilities Execute quality iterations on reference agents identifying potential model or agent framework improvements.

Source or facilitate creation of datasets for evaluation and training to improve model performance for Cloud customers.

Design new agentic systems and context engineering algorithms, utilizing existing models and identifying the need for new training/evaluation objectives where necessary.

Define and implement metrics that correspond to business problems.

Prototype and iterate on the solution working closely with customers, product management and business development.

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