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Privacy Preserving Machine Learning Jobs (NOW HIRING)

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

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 ...

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 ...

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 ...

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 ...

Sr. Machine Learning Engineer Are you interested in enhancing the capabilities of Siri and Apple ... secure, and privacy-preserving experiences, simultaneously delivering Apple-level design and ...

... 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 ...

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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 Jun 13, 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, and why are they important?

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.
More about Privacy Preserving Machine Learning jobs
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:

Machine Learning Research Scientist

Dynamis Labs

San Francisco, CA • On-site

$120K - $300K/yr

Full-time

Medical, Dental, Vision

Posted yesterday


Job description

Position Overview
Sentra is building organizational superintelligence through memory infrastructure that reasons across time, causality, and context. As a Research Scientist, you will tackle fundamental problems in knowledge representation, temporal reasoning, and semantic compression. You will design and implement systems that maintain execution state for entire organizations, consolidate millions of micro-events into durable knowledge, and learn patterns that predict events before it happens.
Key Responsibilities
  • Build LLM-powered information extraction pipelines that process unstructured communications and text data into structured entity-relationship representations.
  • Develop memory consolidation algorithms that validate information through multiple observations, merge duplicate entities, and prune ephemeral data.
  • Design temporal knowledge graph architectures that model organizational execution state as living, continuously updated systems rather than static records.
  • Create graph attention mechanisms and reasoning systems for complex causal queries about blockers, dependencies, and outcome patterns.
  • Research lossy semantic compression using information-theoretic principles to condense event streams into query-relevant long-term memory.
  • Design entity resolution systems handling identity evolution where entities merge, split, and transform through time.
  • Build meta-learning systems that identify organizational patterns and recognize when current situations match historical success or failure indicators.
  • Develop privacy-preserving cross-organizational learning using federated learning and differential privacy techniques.
  • Publish research findings and contribute to the broader research community on knowledge graphs and organizational intelligence.

Must-have Requirements
  • 5+ years building novel systems in machine learning, NLP, knowledge graphs, or related areas with evidence through publications, production implementations, or significant open-source contributions.
  • Deep knowledge of knowledge graphs, graph neural networks, or temporal reasoning demonstrated through shipped systems and architectural exploration.
  • Strong ML and NLP foundation, particularly in information extraction, entity resolution, or semantic representation.
  • Proficiency in Python and modern ML frameworks (PyTorch preferred) with experience deploying models at scale.
  • Track record of publishing research (conference papers, technical blog posts, or detailed technical documentation) and exploring novel architectures.
  • Ability to move between theoretical investigation and practical implementation, shipping research into production.

Bonus skills:
  • Graph databases (Neo4j, TigerGraph, Neptune) and query optimization for large-scale graphs.
  • Information theory, compression, or temporal data structures.
  • Causal inference, probabilistic reasoning, or Bayesian methods.
  • Distributed systems, stream processing, or real-time ML serving.
  • Human memory and cognition models.
  • Privacy-preserving ML (federated learning, differential privacy, secure multi-party computation).
  • Enterprise AI systems, workflow automation, or organizational software.
  • Publications at top-tier conferences (NeurIPS, ICML, ICLR, KDD, EMNLP, ACL, WWW, SOSP, OSDI).

Compensation and Benefits
  • Base Salary: $150,000 - $300,000
  • Equity: 0.3% - 2% depending on level
  • Comprehensive Health Coverage: Medical, dental, and vision
  • Wellness & Productivity Stipend: $2,500/month to cover meals, transport, gym memberships, or other personal productivity needs
  • Hardware & Tools: Latest MacBook Pro and AI development tools (ChatGPT Pro, Claude Pro, Cursor, etc.)
  • Learning & Growth: Dedicated budget for conferences, courses, and professional development
  • Relocation Support: Available for on-site hires
  • Flexible Time Off Policy

Total estimated annual benefits package: ~$30K-$35K in addition to base and equity.