Applied AI Software Engineer, Data Engineering Agent

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.

• 2 years of experience with software development or 1 year of experience with

an advanced degree in an industry setting.

• 2 years of experience with developing large-scale infrastructure, distributed

systems or networks, or experience with compute technologies or storage.

• Experience with the following: Backend, Distributed Systems, Machine Learning

Infrastructure.

PREFERRED QUALIFICATIONS:

• Master's degree or PhD in Computer Science or related technical fields.
• Experience programming in Java, C++, or Python.
• Experience with Generative AI Agent, AI Platform, Machine Learning, or

Intelligent Agent.

• Experience using collaborative notebook-based workflows (e.g. Jupyter) for

prototyping and knowledge sharing.

• Expertise in AI/ML product and platform development, quality evaluation

framework and improvement.

• Passionate about delighting enterprise customers, and an excellent

understanding of enterprise workloads.

ABOUT THE JOB:

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.

We are part of Data Cloud where we are responsible for developing agentic

ELT/ETL capabilities to orchestrate and manage data processing end-to-end. Our

mission is to accelerate and democratize our customers’ journey to digital

insights and AI.

The Data Engineering Agent team's mission is to democratize data engineering

expertise, making it accessible to every data worker. In this role, you will be

working on building agent harness, reasoning engine, skills, evals, agent APIs,

Gemini integrations and evaluation-framework for Data Engineering Agents. You

will have the opportunity to work with BigQuery, Data Integration, Google Cloud

Platform (GCP) Data Analytics BI, GCP Titan/Gemini, Vertex AI and other GCP

services and work across domains of machine learning platforms and algorithms,

distributed systems among others.

Google Cloud accelerates every organization’s ability to digitally transform its

business and industry. We deliver enterprise-grade solutions that leverage

Google’s cutting-edge technology, and tools that help developers build more

sustainably. Customers in more than 200 countries and territories turn to Google

Cloud as their trusted partner to enable growth and solve their most critical

business problems.Individual pay is determined by factors including job-related

skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

• Write product or system development code.
• Participate in, or lead design reviews with peers and stakeholders to decide

amongst available technologies.

• Review code developed by other developers and provide feedback to ensure best

practices (e.g., style guidelines, checking code in, accuracy, testability,

and efficiency).

• Contribute to existing documentation or educational content and adapt content

based on product/program updates and user feedback.

• Triage product or system issues and debug/track/resolve by analyzing the

sources of issues and the impact on hardware, network, or service operations

and quality.