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Differential Privacy Jobs (NOW HIRING)

Familiarity with privacy-preserving machine learning techniques, such as Differential Privacy, Federated Learning, or Homomorphic Encryption. * Experience building first-class user facing products.

Privacy-preserving computation (e.g., secure enclaves, MPC, differential privacy) * Security and adversarial systems * Machine learning safety or alignment * Experience designing robust systems under ...

... differential privacy, multi-party computation, and homomorphic encryption to deliver utility while protecting users C++ and Swift programming Experience using large language models for software ...

Senior Researcher

New York, NY · On-site

$225K - $300K/yr

Perform cutting-edge research in market microstructure, mechanism design, and/or differential privacy . * Be a part of research breakthroughs with opportunities to realize your ideas in our products ...

Translate complex privacy-enhancing technologies-differential privacy, secure multi-party computation, federated learning-into practical product capabilities that clients can actually use * Partner ...

Translate complex privacy-enhancing technologies-differential privacy, secure multi-party computation, federated learning-into practical product capabilities that clients can actually use * Partner ...

Translate complex privacy-enhancing technologies-differential privacy, secure multi-party computation, federated learning-into practical product capabilities that clients can actually use * Partner ...

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Showing results 1-20

Differential Privacy information

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$99.5K

$115.5K

$129.5K

How much do differential privacy jobs pay per year?

As of Jun 7, 2026, the average yearly pay for differential privacy in the United States is $115,505.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,000.00 and $129,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by professionals working in differential privacy roles?

Professionals in differential privacy often encounter challenges balancing data utility with privacy guarantees, as stricter privacy controls can limit the usefulness of data for analysis. They also need to stay updated on evolving privacy regulations and technological advancements. Collaboration with data scientists, engineers, and legal teams is essential to ensure solutions meet both technical and compliance requirements. Additionally, translating complex mathematical concepts into practical, scalable systems that integrate smoothly with existing infrastructure can be a significant hurdle.

What is differential privacy?

Differential privacy is a mathematical framework used to ensure that individual data remains private when analyzing and sharing aggregate information from a dataset. It introduces controlled random noise to the results of queries or computations, making it difficult to determine whether any specific individual's data is included. This helps organizations gain insights from data while providing strong privacy guarantees for individuals, even against attackers with access to other information. Differential privacy is widely used in fields such as statistics, machine learning, and data publishing.

What is the difference between Differential Privacy vs Data Scientist?

AspectDifferential PrivacyData Scientist
Primary FocusProtecting individual data privacy in datasetsAnalyzing and interpreting complex data to inform business decisions
Required SkillsMathematics, privacy algorithms, data securityStatistics, programming, data visualization
Work EnvironmentResearch labs, tech companies, privacy-focused organizationsBusiness, tech firms, consulting
CertificationsPrivacy certifications, data security credentialsData science certifications, programming skills

While Differential Privacy focuses on implementing privacy-preserving techniques in data handling, Data Scientists analyze data to extract insights. Both roles require strong technical skills, but their core objectives differ: one emphasizes privacy protection, the other data analysis.

What are the key skills and qualifications needed to thrive as a Differential Privacy Engineer, and why are they important?

To thrive as a Differential Privacy Engineer, you need a strong background in mathematics, statistics, computer science, and experience with privacy-preserving algorithms, usually supported by an advanced degree. Familiarity with programming languages like Python or R, privacy frameworks (such as Google's DP library), and knowledge of data security regulations are typically required. Excellent problem-solving skills, attention to detail, and the ability to communicate complex concepts to non-experts are crucial soft skills. These competencies are vital to designing robust privacy solutions that protect user data while enabling meaningful data analysis.
More about Differential Privacy jobs
What cities are hiring for Differential Privacy jobs? Cities with the most Differential Privacy job openings:
What states have the most Differential Privacy jobs? States with the most job openings for Differential Privacy jobs include:
Infographic showing various Differential Privacy job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 79% Full Time, 19% Part Time, and 1% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $115,505 per year, or $55.5 per hour.
Staff Software Engineer, Privacy

Staff Software Engineer, Privacy

Waymo

Mountain View, CA • On-site, Remote

$251K - $310K/yr

Other

Posted 16 days ago


Job description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver-to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

As Waymo prepares to bring the Waymo Driver to European markets, we are seeking a Privacy Engineer to lead the technical architectural design and implementation of our global privacy systems.

In this role, you will bridge the gap between legal requirements (GDPR) and technical reality. You will not just "manage" compliance; you will build the actual engines, data pipelines, and anonymization frameworks that allow our autonomous vehicles to operate in strict regulatory environments. You will work closely with Legal, Product, and AI/ML teams to ensure that Privacy by Design is baked into our sensor data ingestion, mapping technologies, and rider experience from day one.

You will:

  • Architect for EU Compliance: Design and build data handling systems that satisfy strict GDPR requirements, "Right to be Forgotten" automation, and consent management frameworks for European riders.
  • Sensor Data Anonymization: Develop and optimize computer vision and ML pipelines to automatically detect and blur faces, license plates, and other PII from camera/Lidar data at the edge (on-vehicle) and in the cloud before it enters our training sets.
  • Privacy Infrastructure: Write scalable code (C++/Java/Kotlin) to enforce retention policies, access controls, and data minimization across Waymo's distributed infrastructure.
  • Partner with Waymo's Legal and Policy teams to translate complex regulatory texts (e.g., the EU Data & AI Act) into concrete engineering specifications and system requirements.
  • Threat Modeling: Conduct privacy impact assessments (PIA) and threat modeling for new features, specifically focusing on cross-border data transfer risks and third-party vendor integration.

You have:

  • BS degree in Computer Science, or a related technical field, or equivalent practical experience.
  • 8+ years of experience in software engineering with a focus on privacy, security, or data infrastructure.
  • Proficiency in one or more general-purpose programming languages (e.g., Python, C++, Java).
  • Deep understanding of GDPR.
  • Experience working with large-scale data processing frameworks (e.g., Flume, MapReduce, etc..) and cloud infrastructure.

We prefer:

  • Prior experience helping a US-based tech company launch products in the European Economic Area (EEA) or UK.
  • Familiarity with privacy-preserving machine learning techniques, such as Differential Privacy, Federated Learning, or Homomorphic Encryption.
  • Experience building first-class user facing products.

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.

Salary Range
$251,000—$310,000 USD