Staff Software Engineer, Private Machine Learning
Job Summary
Job Description
- 8+ years software development experience
- 5+ years ML recommendation systems (Ads)
- 3+ years software design & architecture
- Experience with open-source technologies
- Lead and mentor engineering teams
- Master's/PhD in relevant field
- Experience in Privacy Preserving ML
- Technical leadership experience
- Experience in complex, matrixed organizations
- Bonus
- Equity
- Benefits
5 skills required
Job Details
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience with machine learning recommendation systems in the context of Ads.
- 3 years of experience with software design and architecture.
- Experience with open source technologies.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/algorithms.
- 3 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.
- Experience in Privacy Preserving Machine Learning, Privacy Preserving System Design or Trusted Execution Environments.
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 join the team in the early stages with the opportunity to shape the future of private advertising, and you will have the opportunity to research, prototype, build, and ship innovative features for creating secure and private machine learning solutions for ads machine learning problems.
The Privacy Sandbox initiative aims to create technologies that both protect people's privacy online and give companies and developers tools to build thriving digital businesses. Our team is dedicated to strengthening the Web and Android platform against tracking and fingerprinting, while also developing new privacy-preserving APIs to support personalization and measurement on the web. We are committed to collaborating with stakeholders in the industry to develop and implement viable, alternative solutions in a way that protects the privacy of users while also ensuring the continued vitality of the web.
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.
Responsibilities
- Research and understand different ML training, advertising use cases in the industry, design and implement ML simulations to represent those problems in our custom ML training setup.
- Influence, coach and lead a team of distributed engineers to design experiments to understand eligible training techniques better, quantify model quality and run experiments in a reliable fashion.
- Think about the impact of DP and confidential computing technologies like TEEs on the ML training characteristics and related costs. Think about open-source implementations of these solutions and platformize them for public usage.
- Think about private solutions, relying on techniques like DP-SGD, to solve those ML optimization problems. Collaborate closely with Google research on shipping private modeling techniques for the ads ecosystem.
- Design, develop, test, deploy, maintain, and enhance large scale software solutions.
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About The Company
A problem isn't truly solved until it's solved for all. Googlers build products that help create opportunities for everyone, whether down the street or across the globe. Bring your insight, imagination and a healthy disregard for the impossible. Bring everything that makes you unique. Together, we can build for everyone.
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