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Privacy Engineering Jobs (NOW HIRING)

Senior Privacy Engineer

OR · On-site +1

$104K - $143K/yr

Upstart's Privacy Engineering team builds the systems, tools, and technical controls that help protect borrower, applicant, partner, and Upstarter data across our products and platforms. The team ...

Privacy Engineer, User Privacy

Cupertino, CA · On-site

$124K - $159K/yr

The Privacy Engineering team works with teams all across the company to make sure that products and services protect user privacy by designing architectures that reduce the exposure of user data at ...

Senior Privacy Engineer

$125K - $165K/yr

Upstart's Privacy Engineering team builds the systems, tools, and technical controls that help protect borrower, applicant, partner, and Upstarter data across our products and platforms. The team ...

The Privacy Engineering team works with teams all across the company to make sure that products and services protect user privacy by designing architectures that reduce the exposure of user data at ...

Privacy Solution Architect

Dallas, TX · On-site

$62.50 - $82.50/hr

We are seeking a hands-on Privacy Engineering expert who can help refine the concept, define the technical architecture, and build an MVP for a global connected vehicle privacy platform that empowers ...

New

Our Team The Privacy & Data Protection Engineering (PDPE) organization at Netflix ensures responsible data use across the company and provides the guidance and tooling to help teams move fast with ...

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Privacy Engineering information

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$99.5K

$115.5K

$129.5K

How much do privacy engineering jobs pay per year?

As of Jul 21, 2026, the average yearly pay for privacy engineering 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 does a privacy engineer do?

A privacy engineer designs and implements systems to protect user data and ensure compliance with privacy laws and regulations. They work with software development teams to embed privacy features, conduct risk assessments, and utilize tools like data encryption and anonymization. Strong knowledge of privacy frameworks and programming skills are essential for this role.

What is privacy engineering?

Privacy engineering is the discipline of designing and implementing systems, processes, and technologies that protect users' personal data and ensure compliance with privacy regulations. Privacy engineers work to embed privacy protections directly into products and services, often by using techniques like data minimization, anonymization, and encryption. They collaborate with legal and compliance teams to understand regulatory requirements and translate them into technical solutions. The goal is to proactively mitigate privacy risks and build trust with users by safeguarding their information.

How much does a privacy engineer make?

The average salary for a privacy engineer typically ranges from $100,000 to $150,000 annually, depending on experience, location, and industry. Senior privacy engineers with specialized skills or certifications can earn higher salaries, often exceeding $180,000 per year.

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

To thrive as a Privacy Engineer, you need a strong background in computer science, data security, and privacy regulations such as GDPR or CCPA, typically supported by a degree in a related field. Familiarity with privacy-enhancing technologies, encryption tools, and compliance management systems is essential, with certifications like CIPT or CISSP being advantageous. Strong analytical thinking, problem-solving abilities, and effective cross-functional communication skills help you translate privacy requirements into practical technical solutions. These competencies are crucial for designing systems that protect user data, ensure regulatory compliance, and maintain organizational trust.

What is the difference between Privacy Engineering vs Data Privacy Analyst?

AspectPrivacy EngineeringData Privacy Analyst
Required credentialsTypically requires degrees in computer science, cybersecurity, or related fields; certifications like CIPP, CIPM are commonOften requires degrees in law, compliance, or related fields; certifications like CIPP, CIPM are also common
Work environmentFocuses on designing and implementing privacy features within systems and productsFocuses on monitoring, auditing, and ensuring compliance with privacy policies
Employer and industry usageUsed in tech companies, software development, and organizations building privacy-centric productsUsed in legal, compliance departments, and organizations managing data privacy regulations

Privacy Engineering involves designing technical solutions to protect user data, while Data Privacy Analysts focus on policy compliance and data audits. Both roles are essential in maintaining data privacy but differ in their core responsibilities and skill sets.

What engineers make $500,000?

Senior privacy engineers, especially those with extensive experience, specialized skills in data protection, and certifications like CISSP or CIPP, can earn salaries around or above $500,000 annually in large tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their critical role in safeguarding sensitive information.

What engineers make $300,000 a year?

Senior privacy engineers, especially those with extensive experience, advanced certifications, and expertise in security tools and compliance, can earn $300,000 or more annually. High compensation is often associated with leadership roles, specialized skills, and working in large technology companies or consulting firms.

What are some common challenges privacy engineers face when implementing privacy-preserving technologies within large organizations?

Privacy engineers often encounter challenges such as aligning technical solutions with evolving legal requirements (like GDPR or CCPA), integrating privacy-preserving features into legacy systems, and balancing user experience with robust data protection. Collaboration with cross-functional teams—including legal, security, and product management—is essential to ensure compliance without hindering innovation. Additionally, privacy engineers must stay updated on the latest tools and frameworks to effectively manage risks and implement scalable solutions.
More about Privacy Engineering jobs
What cities are hiring for Privacy Engineering jobs? Cities with the most Privacy Engineering job openings:
What states have the most Privacy Engineering jobs? States with the most job openings for Privacy Engineering jobs include:
Infographic showing various Privacy Engineering job openings in the United States as of July 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% 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

Anthropic

San Francisco, CA • On-site

$405K - $485K/yr

Full-time

PTO

Re-posted 10 days ago


Job description

About Anthropic
Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.
About the role
Anthropic is working on frontier AI systems that handle sensitive information at enormous scale. How we protect that data - and how we build privacy into our systems rather than bolting it on afterward - is central to our mission of building AI that is safe and beneficial.
This is a foundational role. As one of our first dedicated privacy engineers, you will help establish the privacy engineering function at Anthropic and shape how privacy is designed into our AI systems from the ground up. You'll architect privacy-preserving systems, lead the implementation of privacy-enhancing technologies across our infrastructure, and provide technical leadership on privacy across engineering, research, and product teams.
You'll work at the intersection of privacy engineering, AI safety, and distributed systems, solving problems that don't yet have established answers. This is a senior individual contributor role with high autonomy and broad influence.
Key responsibilities
  • Design and implement privacy-preserving architectures for AI training and inference systems operating at very large scale, using techniques such as differential privacy, federated learning, and secure multi-party computation
  • Partner with researchers to implement privacy-preserving training methods that protect user data while maintaining model quality
  • Build foundational privacy infrastructure, including automated data discovery, classification, access controls, audit logging, and lifecycle management
  • Translate regulatory requirements (e.g., GDPR, CCPA, HIPAA, the EU AI Act) into technical implementations and automated compliance controls
  • Architect data governance systems for tracking data lineage, purpose limitation, and retention across distributed AI systems
  • Lead privacy reviews and threat modeling for new models and features, identifying risks and designing scalable mitigations
  • Partner with product and infrastructure teams to embed privacy controls into Claude's inference systems, user interfaces, and data pipelines
  • Develop privacy engineering toolkits and frameworks that enable other engineers to build privacy-preserving features by default
  • Design privacy-preserving analytics and measurement systems that surface useful insights without exposing individual user data
  • Evaluate emerging privacy technologies from academia and industry, and contribute to open-source tooling and AI privacy standards
  • Advise on and advocate for privacy practices as a core part of how we approach AI safety
Minimum qualifications
  • Experience applying privacy engineering principles in production systems, including privacy by design, data minimization, and purpose limitation
  • Proficiency in Python, Go, or similar languages, with experience building and operating production systems at scale
  • Experience designing and implementing privacy infrastructure for systems with a large user base
  • Experience with data governance, classification, or data lifecycle management systems
  • Understanding of privacy regulations such as GDPR and CCPA, and the ability to translate legal requirements into technical designs
  • Experience conducting privacy reviews, threat modeling, or risk assessments
  • Written and verbal communication skills sufficient to drive alignment across engineering, research, legal, and product teams
Preferred qualifications
  • Hands-on experience with privacy-enhancing technologies (e.g., differential privacy, homomorphic encryption, secure enclaves, secure multi-party computation)
  • Experience building privacy infrastructure or controls for machine learning or AI systems
  • Experience establishing a privacy engineering practice, or being an early hire in a function
  • Experience with distributed systems and cloud infrastructure at scale
  • Experience serving as a technical lead on complex, multi-quarter projects
  • Contributions to open-source privacy tooling, privacy research, or industry standards
  • 12+ years of experience in a software engineering role, including building and operating large-scale infrastructure
  • 3+ years of experience leading large, complex projects as a technical lead

The annual compensation range for this role is listed below.
For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.
Annual Salary:
$405,000-$485,000 USD
Logistics
Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience
Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links-visit anthropic.com/careers directly for confirmed position openings.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact - advancing our long-term goals of steerable, trustworthy AI - rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.