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Research Machine Learning Federated Learning Jobs in Texas

Q2

Machine Learning Engineer

Austin, TX

  • Medical

... research and experimentation to advance machine learning capabilities Collaborate with cross-functional teams to integrate AI solutions into production environments Analyze large datasets to extract ...

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

  • Medical

  • Dental

  • Retirement

  • PTO

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team ... Strong communicator who can translate complex research findings into actionable decisions for ...

Q2 India

Machine Learning Engineer

Austin, TX · On-site

$100 - $130/hr

  • Medical

RESPONSIBILITIES** • Design and implement machine learning algorithms and models for various business applications • Conduct research and experimentation to advance machine learning capabilities ...

Q2 Software, Inc.

Machine Learning Engineer

Austin, TX · On-site

  • Medical

... research and experimentation to advance machine learning capabilities • Collaborate with cross-functional teams to integrate AI solutions into production environments • Analyze large datasets to ...

Dow

Machine Learning Engineer

Houston, TX · On-site

  • Medical

  • Life

  • Retirement

  • PTO

As a Machine Learning Engineer in our Data & Analytics Platforms team specializing in data and analytics solutions , you will work with a cross-functional team whose objective is to deliver solutions ...

Rivago infotech inc

Machine Learning Engineer

Manor, TX · On-site

Experience in designing and maintain the infrastructure, CI/CD pipelines, and monitoring systems that take machine learning models from experimental research to scalable, production-ready enterprise ...

New

Dow

Machine Learning Engineer

Houston, TX · On-site

$120 - $160/hr

  • Medical

  • Life

  • Retirement

  • PTO

As a Machine Learning Engineer in our Data & Analytics Platforms team specializing in data and analytics solutions, you will work with a cross-functional team whose objective is to deliver solutions ...

Socket.dev

Sr. Machine Learning Engineer

Austin, TX · On-site

$170 - $210/hr

  • Medical

  • Dental

  • Vision

  • Retirement

As a Senior Machine Learning Engineer, you'll take a leading technical role in building the ... Design, build, and expose ML-powered capabilities through GraphQL APIs and federated subgraphs ...

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Showing results 21-40

All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Texas

Research Machine Learning Federated Learning information

What are the key skills and qualifications needed to thrive as a researcher in machine learning federated learning?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is a researcher in machine learning federated learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Research Machine Learning Federated Learning jobs in Texas?

For Research Machine Learning Federated Learning jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Research Machine Learning Federated Learning jobs in Texas look for?

The top searched job categories for Research Machine Learning Federated Learning jobs in Texas are:

What cities in Texas are hiring for Research Machine Learning Federated Learning jobs?

Cities in Texas with the most Research Machine Learning Federated Learning job openings:

Research Machine Learning Federated Learning jobs near you

Machine Learning Engineer, Wallet Intelligence and Machine Learning

Apple

Austin, TX • On-site

Full-time

Posted 28 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

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

Apple logo

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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