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AI Engineer Jobs
Google

Senior Staff Software Engineer, Search Ads, AdsML, LLM

Google

Location
Onsite (Mountain View, CA · Pittsburgh, PA)
Compensation
$262k - $364k/yr
Employment
Full-time
Level
Senior Level
Posted 6 days ago

About the Role

Google Ads is seeking a Senior Staff Software Engineer to develop next-generation machine learning infrastructure and modeling techniques for search ads. You will prototype new models, improve system robustness, and collaborate with cross-functional teams to integrate solutions across the ecosystem.

Skills

Python C++ Machine Learning Infrastructure LLMs GenAI System Design Data Structures Algorithms Technical Leadership Information Retrieval Distributed Computing Natural Language Processing Model Deployment Model Evaluation Data Processing

Benefits

  • Health Insurance
  • Equity
  • Bonus

Perks

  • Bonus
  • Equity

Full job details

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 8 years of experience programming in Python or C++.
  • 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; and testing/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.
  • 8 years of experience with data structures and algorithms.
  • 5 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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

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: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

  • Work across many different aspects of machine learning modeling and infrastructure. Responsibilities include: engaging with modeling teams to explore these modeling techniques across many different applications.
  • Prototype new modeling techniques. Analyze and debug quality and cost, and making improvements.
  • Change infrastructure (training and serving) to support new modeling techniques. Improve robustness, reliability, usability of infrastructure.
  • Collaborate closely with other members of our team and partners to build systems that take advantage of and integrate seamlessly across our ecosystem. Engage with modeling teams to explore these modeling techniques across many different applications.