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Privacy Preserving Machine Learning Jobs in Texas

webAI Inc

AI Research Scientist

Austin, TX · On-site

... privacy-preserving. Responsibilities: * Design, train, and optimize machine learning models including LLMs, multimodal models, transformers, and diffusion architectures * Conduct research on model ...

webAI

AI Research Scientist

Austin, TX · On-site

Responsibilities : • Design, train, and optimize machine learning models including LLMs ... learning • Familiarity with privacy-preserving ML techniques such as federated learning • ...

webAI Inc

Director of Applied AI

Austin, TX · On-site

Master's or PhD in a technical discipline such as Computer Science, Machine Learning, AI or Applied Mathematics. * Background in distributed systems, edge computing, or privacy-preserving ML.

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

Workiva

$40/hr

As a Machine Learning Engineering Intern, you will be part of a collaborative team supporting the ... Workiva employees are required to undergo comprehensive security and privacy training tailored to ...

New

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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 Texas?

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

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

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

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

Cities in Texas 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 Texas as of August 2026, with employment types broken down into 85% Full Time, and 15% Contract. Highlights an 66% In-person, and 34% Remote job distribution.

Machine Learning Engineer, Wallet Intelligence and Machine Learning

Apple

Austin, TX • On-site

Full-time

Re-posted 17 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 680 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

Are you motivated to protect users and their accounts while delivering the best possible customer experience? Come join the Wallet Intelligence and Machine Learning team, where we help secure users' digital lives across Apple's devices without sacrificing privacy. Machine Learning Engineers here build analytical solutions and think deeply about where they fit into a larger system, staying ahead of fraud and applying the best privacy-preserving and fraud-prevention methods available to make Apple products, and especially Apple Pay and Apple Wallet, the safest platform people can use.
The On Device Insights team at Apple develops machine learning models that run directly on users' devices to protect them from fraud, holding themselves to an exceptionally high bar for privacy. As part of the Wallet Intelligence and Machine Learning team, you will help secure users' digital lives across Apple's devices - including Apple Pay and Apple Wallet - without sacrificing privacy. This is a mission-driven team that thrives on hard problems, healthy skepticism, and open collaboration.
Description
We are looking for a Machine Learning Engineer to help develop and launch on-device technologies that keep our users safe, working closely with engineering, security, program management, and business partners.
Our work is applied and pragmatic by necessity. Models must run in real time and in the background on the device without slowing down something as simple as an in-app purchase, which means designing within real constraints like model size, inference budgets, and memory. Because we often need to anticipate fraud rather than react to each new pattern as it appears, we have to be proactive and think ahead. This role is a chance to take ownership of a problem area, build a system-wide understanding of where our models fit, and apply your expertise in machine learning in an innovative and fast-moving environment.
If you're energized by ambiguity, motivated by a meaningful mission, and the kind of person who digs beneath the surface and questions your own assumptions before forming a recommendation, we'd love to hear from you.
Minimum Qualifications
Experience with machine learning methods such as classification, clustering, and anomaly detection.
Strong programming skills in one or more languages such as Python, Scala, or Java.
Experience processing and analyzing data at scale using distributed data or compute frameworks.
Ability to communicate the results of analysis clearly and succinctly to a range of audiences.
Experience delivering results on ambiguous, loosely defined problems, working with others.
Rigorous analytical thinking, including the ability to question assumptions, reason through a problem, and justify a recommendation with sound evidence.
Preferred Qualifications
Experience deploying machine learning in resource-constrained or real-time environments, such as on-device deployment, model compression, or optimizing for inference budgets.
Experience with distributed data and compute frameworks such as Spark, Ray, or Daft.
Familiarity with privacy-preserving machine learning techniques.
Background in fraud detection, risk modeling, or security-focused machine learning.
Familiarity with iOS development.
We're open to a range of specializations and are excited by candidates who bring a differentiating strength to the team, whether that's a research background, deep systems thinking, or expertise we don't yet have. Tell us what you'd add.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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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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