Staff Applied ML Software Engineer, Recommendations, YouTube

About the role

MINIMUM QUALIFICATIONS:

• Bachelor's degree in Computer Science or a related field, or equivalent

practical experience.

• 8 years of experience in software development.

• 5 years of experience building and deploying recommendation systems models

(retrieval, prediction, ranking, embedding) in production, and experience

building architecture in different modeling domains.

• 5 years of experience with ML design and ML infrastructure (e.g., model

deployment, model evaluation, data processing, debugging, fine tuning).

• 3 years of experience in Applied Machine Learning.
• Experience with publications in ML, or a related field.

PREFERRED QUALIFICATIONS:

• Master's degree or PhD in Computer Science or related field.
• 8 years of experience with data structures and algorithms.
• 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.

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.

YouTube recommendation systems suggests videos that maximizes value to our

viewers. We have cutting edge technology and ML based algorithms to retrieve and

rank the best possible videos given the user context and preferences. Our

recommendation stack is constantly evolving to keep up with the newest

advancements in ML technology (e.g., transformers, foundation, Gemini models).

In this role, you will require very deep knowledge of ML technologies

(especially in the area of recommendations). You will be working on cutting edge

algorithms and optimization techniques. You will also require prior ML

experience. You will have a research background in the form of a PhD in ML or

publications in ML or related technical fields (including Physics, Math,

Operations Research, Optimization, etc.).

At YouTube, we believe that everyone deserves to have a voice, and that the

world is a better place when we listen, share, and build community through our

stories. We work together to give everyone the power to share their story,

explore what they love, and connect with one another in the process. Working at

the intersection of cutting-edge technology and boundless creativity, we move at

the speed of culture with a shared goal to show people the world. We explore new

ideas, solve real problems, and have fun — and we do it all together.

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:

• Provide technical leadership on high-impact projects. Manage project

priorities, deadlines, and deliverables.

• Facilitate alignment and clarity across teams on goals, outcomes, and

timelines. Influence and coach a distributed team of engineers.

• Lead the design and implementation of recommendation systems, optimize ML

infrastructure, and guide the development of model architecture.

• Be comfortable analyzing data, drawing conclusions from the logs and using ML

to improve the product.