Software Engineer III, Machine Learning, Privacy Sandbox Full-time

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in C , Python, or Java programming languages.
• 2 years of experience with data structures or algorithms in either an academic or industry setting.
• 2 years of experience with machine learning algorithms and tools (e.g., TensorFlow, Pytorch).

Preferred qualifications:

• Master’s degree or PhD in Computer Science or related technical field.
• 2 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
• Experience developing accessible technologies.
• Experience in code and system health, diagnosis and resolution, and software test engineering.
• Experience with Privacy such as differential privacy, privacy law, device privacy.
• Knowledge of Android internals/Android Open Source Project.

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.

Privacy Sandbox (Android Only) On-Device Personalization explores an innovative way to continue enabling personalization of end-user experiences while providing enhanced protection of end user privacy. It’s built on the simple idea that instead of moving end user data into centralized servers, the personalization logic is pushed to the edge, i.e. on end user’s devices.

Privacy Sandbox offers a suite of primitive building blocks its adopters can use to create their personalization logic at the edge. One such important block is Federated Compute.

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 $136,000-$200,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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

• Design and develop novel training algorithms such as Differentially Private Alternating Minimization (DPAM).
• Validate and benchmark privacy and utility of the novel training algorithms with internal and external datasets.
• Design and develop Confidential Computing based training infrastructure in public cloud infrastructure (e.g., GCP).
• Design and develop other ODP features, such as Federated Learning or Analytics.
• Test and benchmark the whole stack.