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Privacy Preserving Machine Learning Jobs (NOW HIRING)

OVA.Work

Responsible AI Engineer

... of privacy-preserving machine learning techniques. Company : OVA is the most advanced Automated, Intelligent, intuitive On-boarding platform for Staffing Firms of all sizes. Founded in 2018, the ...

Axon

Senior Machine Learning Engineer

Seattle, WA · Hybrid

$139K - $183K/yr

Reports to: Manager, Machine Learning Engineering * Collaborate with scientists and product ... Architect and develop secure, privacy-preserving, solutions to enable the continuous improvement of ...

Apple

Sr. Machine Learning Engineer

Santa Clara, CA · On-site

$143K - $189K/yr

... secure, and privacy-preserving experiences, simultaneously delivering Apple-level design and ... Our team comprises a diverse range of backgrounds, including applied machine learning engineers ...

Waymo

Staff Software Engineer, Privacy

Mountain View, CA · On-site

$251K - $310K/yr

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

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Privacy Preserving Machine Learning information

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$99.5K

$115.5K

$129.5K

How much do privacy preserving machine learning jobs pay per year?

As of Aug 12, 2026, the average yearly pay for privacy preserving machine learning 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 privacy preserving machine learning roles?

Professionals in Privacy Preserving Machine Learning often encounter challenges such as balancing model accuracy with strict privacy requirements, selecting appropriate privacy-preserving techniques (like differential privacy or federated learning), and ensuring compliance with evolving data protection regulations. Collaborative projects may also involve coordinating with legal, data security, and software engineering teams to implement robust solutions. Additionally, staying updated with the latest research and adapting to new threats or vulnerabilities is a continuous part of the role.

What is the difference between Privacy Preserving Machine Learning vs Data Scientist?

AspectPrivacy Preserving Machine LearningData Scientist
Required CredentialsTypically requires knowledge of machine learning, data privacy, and security certificationsRequires degrees in data science, statistics, or related fields; certifications like Certified Data Scientist are common
Work EnvironmentWorks in research, development, and implementation of privacy-focused ML models, often in tech or finance sectorsAnalyzes data, builds models, and provides insights across various industries including marketing, finance, and healthcare
Employer & Industry UsageUsed by organizations prioritizing data privacy, such as healthcare, finance, and tech companiesEmployed across diverse sectors for data analysis, predictive modeling, and decision support

Privacy Preserving Machine Learning focuses on developing models that protect data privacy during training and inference, while Data Scientists analyze and interpret data to generate insights. Both roles require strong analytical skills, but Privacy Preserving Machine Learning emphasizes security and privacy techniques, whereas Data Scientists focus on data analysis and modeling.

What is privacy preserving machine learning?

Privacy preserving machine learning refers to techniques and methods that allow data analysis and model training while protecting sensitive information. This field focuses on ensuring that personal or confidential data is not exposed or compromised during the development and deployment of machine learning models. Approaches such as federated learning, differential privacy, and homomorphic encryption are commonly used. These methods enable organizations to leverage data for insights and predictions without violating privacy regulations or risking data breaches. Privacy preserving machine learning is especially important in industries like healthcare, finance, and any sector handling personal data.

What are the key skills and qualifications needed to thrive as a privacy preserving machine learning engineer?

To thrive as a Privacy Preserving Machine Learning Engineer, you need a strong background in machine learning, data privacy techniques (such as differential privacy or federated learning), and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow Privacy, PySyft, and privacy-enhancing technologies, along with certifications in data security or privacy, are often required. Strong problem-solving abilities, meticulous attention to detail, and the ability to communicate complex technical concepts clearly set top professionals apart. These skills ensure the development of robust machine learning models that protect sensitive data while delivering valuable insights, maintaining compliance and trust.
What cities are hiring for Privacy Preserving Machine Learning jobs? Cities with the most Privacy Preserving Machine Learning job openings:
What states have the most Privacy Preserving Machine Learning jobs? States with the most job openings for Privacy Preserving Machine Learning jobs include:
What job categories do people searching Privacy Preserving Machine Learning jobs look for? The top searched job categories for Privacy Preserving Machine Learning jobs are:
Privacy Preserving Machine Learning jobs near you
Infographic showing various Privacy Preserving Machine Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $115,505 per year, or $55.5 per hour.

Staff Software Engineer, Privacy

Waymo

Mountain View, CA • On-site

Full-time

Re-posted 20 days ago

Job description

Job Summary:
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. They are seeking a Privacy Engineer to lead the design and implementation of global privacy systems, ensuring compliance with GDPR while developing data handling systems and privacy infrastructure for their autonomous vehicles.
Responsibilities:
• 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.
Qualifications:
Required:
• 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.
Preferred:
• 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.
Company:
Waymo is a mobility technology company that improves transportation by developing self-driving solutions for travelers and daily commuters. It is a sub-organization of Alphabet. Founded in 2009, the company is headquartered in Mountain View, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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