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Privacy Preserving Machine Learning Jobs in New York

Snap, Inc.

Privacy Engineer, Level 4

New York, NY · On-site

Evaluate privacy-preserving approaches for machine-learning systems, including leakage and attack risks. * Help define and evolve privacy standards, practices, tooling, and documentation across Snap.

Snapchat

Privacy Engineer, Level 4

New York, NY · On-site

Evaluate privacy-preserving approaches for machine-learning systems, including leakage and attack risks. * Help define and evolve privacy standards, practices, tooling, and documentation across Snap.

New

New York University

Research Engineer

New York, NY · On-site

$34.34/hr

Responsible for researching new techniques for privacy-preserving machine learning. * Expected to contribute to advances in fully homomorphic encryption compiler infrastructure * Expected to ...

MasterCard

Senior Data Scientist

New York, NY

... privacy-preserving techniques, and digital advertising measurement. All About You Experience in Data Science, Machine Learning, AdTech (Preferred), Marketing Science, or a related field is required ...

PTR Global

Blockchain Developer

Woodbridge, NJ · On-site

Implement and optimize privacy-preserving cryptographic primitives including: * Pedersen ... State Machine Design * Design and maintain custom KVStore state machines. * Manage homomorphically ...

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Privacy Preserving Machine Learning information

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 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 are popular job titles related to Privacy Preserving Machine Learning jobs in New York?

For Privacy Preserving Machine Learning jobs in New York, the most frequently searched job titles are:

What job categories do people searching Privacy Preserving Machine Learning jobs in New York look for?

The top searched job categories for Privacy Preserving Machine Learning jobs in New York are:

What cities in New York are hiring for Privacy Preserving Machine Learning jobs?

Cities in New York with the most Privacy Preserving Machine Learning job openings:

Privacy Preserving Machine Learning jobs near you
Infographic showing various Privacy Preserving Machine Learning job openings in New York as of August 2026, with employment types broken down into 86% Full Time, and 14% Contract. Highlights an 74% In-person, and 26% Remote job distribution.

Privacy Engineer, Level 4

Snap, Inc.

New York, NY • On-site

Full-time

Medical

Posted 4 days ago

Job description

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.


The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.


Snap Security teams protect the trust and safety of our global community by securing the systems and data that power Snapchat. We safeguard hundreds of millions of Snapchatters every day, ensuring that every product and service is built on a foundation of security and resilience. Our values guide how we anticipate and mitigate threats, collaborate across Snap, and execute with privacy at the forefront.

We're looking for a Privacy Engineer to join Snap Inc!

What you'll do:

  • Help build privacy into ads ranking and targeting systems.

  • Partner with engineering, product, legal, security, and data teams to translate privacy requirements into practical, production-ready controls that protect users, support our commitments, and preserve product utility

  • Develop and scale privacy-preserving approaches, auditing practices, and technical solutions across Snap's ads ecosystem.

  • Design and implement privacy controls for production systems, considering architecture, scale, performance, reliability, and operational risk.

  • Lead privacy reviews, threat modeling, and risk assessments for products, services, data flows, APIs, and technical designs.

  • Utilize AI tools and high-velocity engineering workflows to ship scalable services while maintaining rigorous standards for correctness, security, and production quality.

  • Support privacy-safe advertising and measurement.

  • Evaluate privacy-preserving approaches for machine-learning systems, including leakage and attack risks.

  • Help define and evolve privacy standards, practices, tooling, and documentation across Snap.

Knowledge, Skills & Abilities:

  • Proficiency in, or a strong aptitude for, using AI tools to streamline development while auditing generated output for architectural integrity, performance bottlenecks, and security risks.

  • Experience within applied privacy techniques such as differential privacy, anonymization, cryptography, privacy-preserving computation, and privacy attack frameworks.

  • Expertise within consent, preference, identity, and tracking technologies, including ATT and opt-out mechanisms; privacy risks in advertising, measurement, machine learning, and embeddings.

  • Software engineering fundamentals, including data structures, algorithms, programming, testing, debugging, and code review.

  • Ability to conduct privacy reviews and threat modeling, document findings, and drive remediation with partner teams.

  • Ability to partner with engineering, product, legal, security, and data teams to reduce risk from design through deployment and remediation.

  • Strong communication skills in order to collaborate across functions, and adapt to evolving AI systems and modern engineering practices.

Minimum Qualifications:

  • Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

  • 2+ years of post-bachelor's privacy, security, software engineering, or related experience; or a master's degree in a technical field plus 1+ year of post-graduate experience; or a PhD in a relevant technical field.

  • Expertise in one or more areas such as privacy engineering, privacy reviews, data governance, privacy-safe advertising or measurement, privacy-preserving machine learning, or security engineering.

Preferred Qualifications:

  • Demonstrated experience securing or privacy-enabling complex distributed systems and production-scale infrastructure.

  • Strong programming and software design skills, including debugging, performance analysis, testing, and automation.

  • Experience with privacy threat modeling, leakage or attack assessments, privacy-control validation, or privacy-preserving ML and embeddings.

  • Strong intellectual curiosity and motivation to keep up with evolving privacy, security, AI, and engineering practices.

If you have a disability or special need that requires accommodation, please don't be shy and provide us some information.

"Default Together" Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a "default together" approach and expect our team members to work in an office 4+ days per week.

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).

Our Benefits: Snap Inc. is its own community, so we've got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap's long-term success!

Compensation

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate's starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

Zone A (CA, WA, NYC):

The base salary range for this position is $157,000-$235,000 annually.


Zone B:

The base salary range for this position is $149,000-$223,000 annually.

Zone C:

The base salary range for this position is $133,000-$200,000 annually.This position is eligible for equity in the form of RSUs.

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