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Differential Privacy Jobs in Virginia (NOW HIRING)

Steampunk

Privacy & Disclosure-Risk Analyst

Mclean, VA · On-site

$83K - $99K/yr

Experience with differential privacy or other privacy-preserving machine learning techniques * Experience with privacy-enhancing technologies, data minimization, de-identification, anonymization, or ...

New

Steampunk

Privacy & Disclosure-Risk Analyst

Mclean, VA

$83K - $99K/yr

Experience with differential privacy or other privacy-preserving machine learning techniques * Experience with privacy-enhancing technologies, data minimization, de-identification, anonymization, or ...

New

Steampunk

Privacy & Disclosure-Risk Analyst

Mclean, VA

$83K - $99K/yr

Experience with differential privacy or other privacy-preserving machine learning techniques * Experience with privacy-enhancing technologies, data minimization, de-identification, anonymization, or ...

New

IQT

Technology Architect - AI

Mclean, VA · On-site

$138K - $180K/yr

Familiarity with privacy-preserving AI, including federated learning, differential privacy, secure aggregation, confidential computing, and multiparty computation. * Understanding of explainable and ...

IQT

Technology Architect - AI

Mclean, VA · On-site +1

Familiarity with privacy-preserving AI, including federated learning, differential privacy, secure aggregation, confidential computing, and multiparty computation. * Understanding of explainable and ...

IQT

Technology Architect - AI

Mclean, VA · Hybrid

Familiarity with privacy-preserving AI, including federated learning, differential privacy, secure aggregation, confidential computing, and multiparty computation. * Understanding of explainable and ...

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Showing results 1-20

All JobsDifferential Privacy JobsDifferential Privacy Jobs in Virginia

Differential Privacy information

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 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 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.

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 popular job titles related to Differential Privacy jobs in Virginia?

For Differential Privacy jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Differential Privacy jobs in Virginia look for?

The top searched job categories for Differential Privacy jobs in Virginia are:

What cities in Virginia are hiring for Differential Privacy jobs?

Cities in Virginia with the most Differential Privacy job openings:

Differential Privacy jobs near you
Infographic showing various Differential Privacy job openings in Virginia as of August 2026, with employment types broken down into 100% Full Time. Highlights an 86% In-person, and 14% Remote job distribution.

Privacy & Disclosure-Risk Analyst

Steampunk

Mclean, VA • On-site

$83K - $99K/yr

Other

Posted 3 days ago

New

Job description

Overview
We are seeking a Privacy & Disclosure-Risk Analyst responsible for evaluating privacy and data disclosure risks associated with AI/ML models and federated learning environments. This role will perform technical privacy testing to identify potential exposure of sensitive or training data through model leakage, membership inference, federated-update reconstruction, and related disclosure-risk techniques.
The Privacy & Disclosure-Risk Analyst will analyze testing results, assess the potential impact of identified privacy vulnerabilities and information exposure, and collaborate with technical teams to identify and validate appropriate risk mitigation measures. This role requires a strong understanding of machine learning concepts, data privacy risks, applicable privacy requirements, and technical approaches for evaluating potential information disclosure from AI/ML systems.
Contributions
  • Assess privacy and disclosure risks associated with AI/ML models, training data, and federated learning environments
  • Conduct model-leakage testing to identify potential exposure of sensitive, protected, or training data
  • Perform membership-inference testing to evaluate whether information about training data can be inferred from model behavior or outputs
  • Conduct federated-update reconstruction testing to evaluate potential disclosure of sensitive information from federated learning workflows
  • Evaluate AI/ML systems for privacy vulnerabilities, unintended information exposure, and potential disclosure risks
  • Develop and execute technical test scenarios and methodologies for evaluating model and data privacy risks
  • Analyze testing results to determine the likelihood, severity, and potential impact of identified disclosure risks, including potential impacts to individuals whose information may be exposed
  • Evaluate the effectiveness of privacy-preserving controls and recommend appropriate risk mitigation measures
  • Collaborate with machine learning engineers, data scientists, cybersecurity teams, and other technical stakeholders to identify and address privacy risks
  • Conduct follow-up testing to validate remediation and privacy risk mitigation measures
  • Document testing methodologies, technical findings, supporting evidence, risk assessments, and recommended mitigation actions
  • Communicate technical privacy and disclosure risks to technical and non-technical stakeholders
  • Maintain awareness of emerging AI/ML privacy attacks, disclosure-risk techniques, privacy-preserving machine learning approaches, and evolving AI privacy and risk management guidance

Qualifications
  • Ability to obtain and maintain a government security clearance
  • Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Cybersecurity, Information Technology, or a related technical discipline, or equivalent relevant experience
  • 5+ years of experience in cybersecurity, data privacy, machine learning, AI/ML security, or related technical disciplines
  • Experience assessing privacy, data exposure, or disclosure risks within technical systems or data environments
  • Strong understanding of machine learning concepts, model training, model outputs, and associated data privacy risks
  • Knowledge of AI/ML privacy attack techniques, including model leakage, membership inference, reconstruction, or related disclosure-risk methods
  • Experience conducting technical security, privacy, data risk, or adversarial assessments
  • Understanding of federated learning or distributed machine learning concepts and associated privacy risks
  • Knowledge of privacy-preserving techniques and controls used to reduce unintended information disclosure
  • Knowledge of AI risk management principles and frameworks, including the NIST AI Risk Management Framework (AI RMF) or similar guidance
  • Ability to analyze technical testing results and assess the potential impact and severity of identified privacy risks
  • Experience documenting technical testing methodologies, findings, risks, and recommended mitigation actions
  • Strong analytical, problem-solving, communication, and collaboration skills

Preferred:
  • Hands-on experience conducting model-leakage, membership-inference, model inversion, reconstruction, or similar AI/ML privacy testing
  • Hands-on experience assessing privacy and disclosure risks within federated learning environments
  • Experience with differential privacy or other privacy-preserving machine learning techniques
  • Experience with privacy-enhancing technologies, data minimization, de-identification, anonymization, or related data protection techniques
  • Experience using Python or other programming languages for technical privacy, security, or machine learning analysis
  • Knowledge of applicable federal privacy laws, regulations, policies, and standards related to sensitive data and information disclosure
  • Familiarity with privacy requirements applicable to health, biomedical, research, or other sensitive data
  • Experience working with sensitive, health, biomedical, research, or other protected data
  • Experience working within federal government or other highly regulated environments
  • Relevant privacy, cybersecurity, data science, or AI/ML certification

About steampunk
Steampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $115,000 to $150,000. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk's total compensation package for employees. Learn more about additional Steampunk benefits here.
Identity Statement
As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
Steampunk is a Change Agent in the Federal contracting industry, bringing new thinking to clients in the Homeland, Federal Civilian, Health and DoD sectors. Through our Human-Centered delivery methodology, we are fundamentally changing the expectations our Federal clients have for true shared accountability in solving their toughest mission challenges. If you want to learn more about our story, visit http://www.steampunk.com.
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Steampunk participates in the E-Verify program.

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