Staff Machine Learning Engineer

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development.
• 5 years of experience testing, and launching software products.
• 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  
• 3 years of experience with software design and architecture.

Preferred qualifications:

• Experience in machine learning, stats, applied math, or operation research in the industry or in the academic sector.
• Experience in productionizing ML systems.
• Experience in Ads, AdsML, LegoML, Keras, or TFX.
• Ability to write high quality and low latency code/models that can train on and serve on every query.

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.

We build and maintain machine learning models Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict user interactions on Search Ads. These models are a key component in setting advertisers' bids, with the goal of improving both satisfaction and Return on Investment (ROI) for Search Ads advertisers using Auto-bidding products. By optimizing towards advertisers' objectives, Auto-bidding products drive Google's global Ads business, which serves billions of users and generates approximately ($X) in business.

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 $197,000-$291,000 + 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

• Learn the bidding machine learning models that drive billions in ad business across Google Ads.
• Innovate and iterate on machine learning model design, improving quality, stability, and efficiency across the entire model life-cycle—from concept to deployment.
• Solve complex machine learning related problems by designing, running, and analyzing experiments using investigative and statistical methods.
• Participate in on-call rotations for model health, working alongside team members to support critical systems.
• Contribute to code health, automation, and alerting systems to ensure model stability and performance.