Software Engineer, Agentic AI and LLMs, Google Ads

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree in Computer Science, Mathematics, a related technical

field, or equivalent practical experience.

• 2 years of experience with software development in one or more programming

languages (e.g., Java, Python, Go, or C++), or 1 year of experience with an

advanced degree.

• 1 year of experience with one or more of the following: Speech/audio systems,

reinforcement learning (sequential decision making), ML infrastructure, or a

distinct specialization in another machine learning field.

• 1 year of experience with end-to-end Machine Learning (including model

deployment, model evaluation, data processing, and debugging).

PREFERRED QUALIFICATIONS:

• Master's degree or PhD in Computer Science, Computer Engineering, Machine

Learning, or related technical fields.

• 2 years of experience with data structures or algorithms in either an

academic or industry setting.

• 1 year of experience with LLMs, prompt design/optimization (e.g. APO), or

modern agentic orchestration frameworks.

• Experience developing accessible technologies that comply with global

usability and accessibility standards.

• Experience with full-stack development across back-end services, database

schemas, APIs, and front-end frameworks (e.g., Dart, JavaScript, or

TypeScript).

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.

Google Ads powers digital commerce for millions of global businesses. Within

Google Ads, the team is creating the next wave of intelligent, autonomous

systems. Rather than relying on static interactions, the team builds

conversational and agentic workflows that resolve complex advertiser inquiries

across chat, email, and virtual meetings. We bridge frontier foundation models

with massive-scale distributed infrastructure to build reliable, helpful, and

safe enterprise assistants.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine

learning and Generative AI models like Gemini to power a multi-billion dollar

global business.

Our work directly impacts billions of users by protecting users from harm,

improving ad quality, and optimizing campaigns for advertiser

return-on-investment. We foster a culture of deep collaboration, partnering

closely with teams like Google Research and DeepMind to solve complex

challenges. Join us to work on state-of-the-art AI, take on problems at an

unparalleled scale, and build the next generation of advertising technology.

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 using Java, Python, Go, or C++ to

build and optimize high-throughput backend services, storage schemas, APIs,

and data pipelines for massive-scale GenAI systems.

• Collaborate with peers and stakeholders through technical design and code

reviews to ensure best practices amongst available technologies, focusing on

style guidelines, checking code in, accuracy, testability, and efficiency.

• Contribute to existing technical documentation or educational developer

content and adapt engineering specs based on system/program updates, prompt

engineering variations, and internal and external user feedback loops.

• Triage issues and debug/track/resolve production anomalies by analyzing the

sources of issues and their impact on hardware, network, or service

operations, system latency, and output quality.

• Implement solutions in specialized ML areas utilizing ML infrastructure to

contribute to model optimization, data processing, and Tier 2 on-call

reliability.