Job Description

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 industry-scale 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.
• 2 years of experience with state of the art GenAI techniques (e.g., 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.
• Experience with technical innovation.
• Experience working within Ads.
• 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 director+ leaders.

About the job

As a Senior Staff SWE, 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.

As an Senior Staff Software Engineer Individual Contributor (TL), 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.

The US base salary range for this full-time position is $262,000-$365,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or 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, client infrastructure teams while balancing specific platform constraints to make decisions that are best for the business overall.