Staff Software Engineer, Youtube Ads Surfaces and Experiences

Google; Town of Montana (WI); On-site; USD 207,000 - 300,000; Full time

Job summary

Google is seeking a Staff Software Engineer for YouTube Ads Surfaces and Experiences to own long-term engineering strategy and drive critical ML initiatives. You will shape system design, mentor engineers, and partner with product and data teams to tackle ambiguous problems at scale.

You will lead cross-functional efforts across ads surfaces, apply GenAI concepts, and influence architecture decisions for a large-scale, safety-conscious platform used by billions of users and advertisers worldwide.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 8 years of software development experience.
• 7 years leading technical project strategy, ML design, and ML infrastructure optimization.
• 5 years of design and architecture with testing and launching software products.
• Experience working within Ads.
• Experience with GenAI techniques (LLMs, multimodal) or related concepts.

Responsibilities

• Drive the long-term engineering strategy and key design decisions.
• Own critical decisions, managing tradeoffs, health, and risk.
• Identify hotspots and invest to remove barriers to productivity and feature throughput.
• Mentor and grow next-gen technical leaders through coaching.
• Collaborate cross-functionally to ship features consistently across surfaces.

Skills

Software development

ML design

Architecture & design

Ads experience

GenAI concepts

Education

Bachelor's degree

Job description

Staff Software Engineer, YouTube Ads Surfaces and Experiences

Location: Mountain View, CA, USA

Experience Level: Advanced

Advanced experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years of experience leading technical project strategy, ML design, and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with design and architecture along with testing and launching software products.
• Experience working within Ads.
• Experience with state of the art GenAI techniques (e.g., Large Language Models (LLMs), Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).

Preferred qualifications:

• Master's degree or PhD in Engineering, Computer Science, or a related technical field.
• Experience leading full stack engineering, distributed systems teams, or quality teams, effectively leading technical strategy and scaling architecture.
• Ability to strategically anticipate and mitigate obstacles in ambiguous or rapidly shifting environments.
• Exceptional communication and collaboration skills to align agendas and strategy with cross-functional directors and leaders.

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.

In this role, you will be responsible for driving the long-term engineering strategy for enabling format optimizations, new experiences, incorporating organizational business priorities, and technological shifts, such as new AI capabilities.

You will act as a de facto authority on major systems and drive the engineering strategy for a critical technical area of great importance to YouTube Ads. Operating with excellent judgment and minimal oversight, you will uncover and address ambiguous, complex, and large-scale technical problems. Your role involves thinking strategically over long time horizons, establishing consensus across divergent teams, and driving technical and process improvements across an organization of over 100+ engineers. You will need to take data-driven approaches along with focusing on making our systems scalable, safer, more reliable, and highly robust.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We're made up of multiple teams, building Google's advertising products including search, display, shopping, travel, and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses to large brands to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US Salary Range: $207,000 - $300,000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Drive the long-term engineering strategy, incorporating organizational business priorities and technological shifts, such as new AI capabilities.
• Own key technical decisions based on your expertise, guiding the team through important design tradeoffs, short-term/long-term strategy, system health, and risk management.
• Identify system hotspots to improve the organization's technical health, ensuring the calculated investments are made to remove widely felt barriers to productivity/feature throughput.
• Invest in growing the next generation of technical leaders through direct coaching and scalable mentorship initiatives.
• Work cross-functionally to ensure features are built consistently across different surfaces, working closely with other teams, quality, and client infrastructure teams while balancing specific platform constraints to make decisions that are best for the business overall.

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