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Privacy Preserving Machine Learning Jobs (NOW HIRING)

SpaceXAI

Application Security Engineer

Palo Alto, CA

$100K - $258K/yr

Familiarity with federated learning and privacy-preserving machine learning techniques * Experience in building custom security tooling to enhance and automate security processes * Interest in ...

Jobgether

Experience with federated learning, privacy-preserving machine learning, or distributed AI systems. * Experience validating predictive toxicity models prospectively and influencing compound design or ...

SpaceXAI

Application Security Engineer

$60.25 - $80.25/hr

... learning and privacy-preserving machine learning techniques • Experience in building custom security tooling to enhance and automate security processes • Interest in leveraging AI to automate ...

Apple

Sr. Machine Learning Engineer - Apple News

Cupertino, CA · On-site

$128K - $177K/yr

At Apple News, our ML problems are uniquely hard, spanning privacy-preserving personalization, on ... Description As a Machine Learning Engineer on the Apple News team, you will build and operate the ...

SpaceXAI

Application Security Engineer

Palo Alto, CA · On-site

$69.25 - $92.50/hr

... learning and privacy-preserving machine learning techniques • Experience in building custom security tooling to enhance and automate security processes • Interest in leveraging AI to automate ...

Apple

Sr. Machine Learning Engineer

Santa Clara, CA

$184K - $324K/yr

... secure, and privacy-preserving experiences, simultaneously delivering Apple-level design and ... As such, we are seeking candidates with applied machine learning experience and strong software ...

Axon

Senior Machine Learning Engineer

Seattle, WA · On-site

$150K - $241K/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 ...

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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.

Machine Learning Engineer - AI & ML Evaluation Frameworks

Apple

Cupertino, CA • On-site

$101K - $135K/yr

Full-time

Re-posted 5 days ago

Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers

Job description

The Health Sensing Machine Learning Interpretability & Analytics (MLIA) team ensures clinical rigor and contextual trust are at the foundation of Apple's health sensing features. We are looking for an exceptional ML Engineer to help us build the next generation of scalable evaluation infrastructure and lead rigorous investigations into model performance. You will develop cutting-edge tools, synthetic data pipelines, and automated frameworks that ensure our health features are mathematically sound, demographically equitable, and clinically safe. If you are passionate about AI safety, robust software architecture, and pushing the boundaries of ML innovation, come join us!
Description
In this role, you will architect and build large-scale evaluation frameworks to interrogate unimodal ML systems and multi-modal foundation models. Beyond infrastructure, you will lead deep-dive ML evaluations, performing failure analysis to uncover performance gaps, reasoning flaws, and edge cases. You will translate findings into actionable insights and work directly with algorithm teams to improve the safety and reliability of our health features. Your work will empower teams across Apple to rapidly evaluate multi-modal sensor fusion while upholding Apple's privacy standards.
Minimum Qualifications
BS in Computer Science, Machine Learning, Statistics, or related field
3+ years of experience in ML Engineering or Applied ML
Strong experience in evaluating supervised, unsupervised, LLMs and deep learning models.
Proficiency in Python with the ability to write production-grade code (OOP, CI/CD, Git)
Hands-on experience in failure analysis, evaluating LLMs and driving subsequent model improvements
Experience building data pipelines, inference frameworks, and automated evaluation systems
Strong communication skills to articulate complex technical concepts across technical and non-technical audiences
Preferred Qualifications
MS/PhD in Computer Science, Machine Learning, Statistics, or related field
Experience evaluating LLMs or agentic systems (e.g., LLM-as-a-judge, RAG evaluation)
Experience with synthetic data generation and prompt engineering
Experience in parallel data processing (Spark, Kubernetes, Airflow) or privacy-preserving ML (Federated Learning)
Background in AI Safety, model interpretability, or adversarial testing
Interest in digital health and clinical rigor

What Apple employees say

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Benefits

Hours and flexibility

Workplace

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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