Staff ML Software Engineer, Search Ecosystem

About the role

MINIMUM QUALIFICATIONS:

• Bachelor’s degree or equivalent practical experience.

• 8 years of experience in software development, including 5 years of

experience testing and launching software products, and 3 years of experience

with software design and architecture.

• 5 years of experience in building ML systems and infrastructure (e.g., for

model training, evaluation, deployment, serving).

• Experience integrating generative AI tools or LLM interfaces into workflows.
• Experience in data processing software (e.g., Hadoop, Spark, Beam, Hive),

strategies (e.g., ETL, ELT, CDC) or algorithms (e.g., MapReduce, Flume).

PREFERRED QUALIFICATIONS:

• Master’s degree or PhD in Engineering, Computer Science, or a related

technical field.

• 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.

• Experience in full-stack development, building back-end distributed

infrastructure, and programming in Java, Python, or C++.

• Experience integrating generative AI tools and understanding of AI

architecture.

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.

The Search Ecosystem team builds a sustainable content ecosystem for Google

products. We develop scalable platforms to address Google's content requirements

by systematically identifying knowledge gaps in our AI models, sourcing

high-quality content to fill them, and then training the models to verify

performance improvements. This is a high-impact, high-visibility growth area

within Search Ecosystems.

This is an opportunity to work on a business-critical product that is redefining

the boundaries of artificial intelligence. We are a fast-moving, growing

organization working on highly impactful projects. In this role, you will help

build robust systems and scalable platforms that power content acquisition to

train AI models in a responsible and secure manner.

In Google Search, we're reimagining what it means to search for information –

any way and anywhere. To do that, we need to solve complex engineering

challenges and expand our infrastructure, while maintaining a universally

accessible and useful experience that people around the world rely on. In

joining the Search team, you'll have an opportunity to make an impact on

billions of people globally.

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

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

RESPONSIBILITIES:

• Develop and implement frameworks and methodologies for evaluating the quality

and effectiveness of AI system components and data.

• Analyze evaluation results to drive improvements in system performance and

underlying value models.

• Play a key role in building and articulating the high-level technical

strategy for your area of ownership.

• Leverage knowledge of LLMs to design, build, and maintain specialized

infrastructure on top of LLMs

• Drive technical projects from ideation through to production, working

effectively in an ambiguous and fast-paced environment.