Software Engineering Manager, Display Ads Bidding

MINIMUM QUALIFICATIONS:

* Bachelor’s degree or equivalent practical experience.

* 8 years of experience in software development.

* 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 in a technical leadership role.

* Experience in statistical analysis or quantitative engineering.

PREFERRED QUALIFICATIONS:

* Master’s degree or PhD in Engineering, Computer Science, Machine Learning,

Statistics, Applied Mathematics, or a related technical field.

* 3 years of experience working in a complex, matrixed organization involving

cross-functional, or cross-business projects.

ABOUT THE JOB:

Like Google's own ambitions, the work of a Software Engineer goes beyond just

Search. Software Engineering Managers have not only the technical expertise to

take on and provide technical leadership to major projects, but also manage a

team of Engineers. You not only optimize your own code but make sure Engineers

are able to optimize theirs. As a Software Engineering Manager you manage your

project goals, contribute to product strategy and help develop your team. Teams

work all across the company, in areas such as information retrieval, artificial

intelligence, natural language processing, distributed computing, large-scale

system design, networking, security, data compression, user interface design;

the list goes on and is growing every day. Operating with scale and speed, our

exceptional software engineers are just getting started -- and as a manager, you

guide the way.

With technical and leadership expertise, you manage engineers across multiple

teams and locations, a large product budget and oversee the deployment of

large-scale projects across multiple sites internationally.

Google Display Ads (GDA) is the trusted way for advertisers to buy display and

video ads on third-party websites and apps that are simple to create, highly

performant, and easy to measure. We manage the advertisers’ spend and deliver

ROI to the advertisers.

In this role, you will

• be on an ML and optimization team powering the GDA
• product. Your team’s mission is to bring state-of-the-art ML and optimization
• techniques to the GDA product.
• 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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
• Learn more about benefits at Google
• [https://www.google.com/about/careers/applications/benefits/].
• RESPONSIBILITIES:
• * Set and communicate team priorities that support the broader organization's
• goals. Align strategy, processes, and decision-making across teams.
• * Set clear expectations with individuals based on their level and role and
• aligned to the broader organization's goals. Meet regularly with individuals
• to discuss performance and development and provide feedback and coaching.
• * Develop the mid-term technical goal and roadmap within the scope of your
• (often multiple) team(s). Evolve the roadmap to meet anticipated future
• requirements and infrastructure needs.
• * Design, guide and vet systems designs within the scope of the broader area,
• and write product or system development code to solve ambiguous problems.
• * Lead the design and implementation of solutions in specialized ML areas,
• optimize ML infrastructure, and guide the development of model optimization
• and data processing strategies.