ZipRecruiter

Log In

1

Research Machine Learning Federated Learning Jobs in Nevada

TensorWave

Senior Machine Learning Engineer

Las Vegas, NV · On-site +1

$117K - $154K/yr

About the Role We're looking for a Senior Machine Learning Engineer to join our team during an exciting phase of growth. In this role, you'll be responsible for building and operating the core ...

TensorWave

Senior Machine Learning Engineer

Las Vegas, NV · On-site

$117K - $154K/yr

About the Role We're looking for a Senior Machine Learning Engineer to join our team during an exciting phase of growth. In this role, you'll be responsible for building and operating the core ...

MNCP Staffing

Learning Specialist

Las Vegas, NV

Research training topics, tools, and emerging learning methods to support content development. Conduct quality control reviews of learning documents and multimedia materials for accuracy and ...

Amh

Learning Specialist

Las Vegas, NV

$30.73 - $34/hr

Research training topics, tools, and emerging learning methods to support content development. Conduct quality control reviews of learning documents and multimedia materials for accuracy and ...

Deloitte

AI Solutions Architect

Las Vegas, NV · On-site

$60.25 - $79.25/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning, automation, and data-driven Human Capital engagements * Developing account growth strategies, managing ...

Ormat Technologies, Inc.

Geochemist

Reno, NV

Introduction Headquartered in Reno, NV, Ormat designs, develops, builds, owns and operates geothermal and recovered energy-based power plants in the US and worldwide. With a spotless international ...

next page

Showing results 1-20

All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Nevada

Research Machine Learning Federated Learning information

What are the key skills and qualifications needed to thrive as a Researcher in Machine Learning Federated Learning, and why are they important?

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 Nevada? For Research Machine Learning Federated Learning jobs in Nevada, the most frequently searched job titles are:
What job categories do people searching Research Machine Learning Federated Learning jobs in Nevada look for? The top searched job categories for Research Machine Learning Federated Learning jobs in Nevada are:
What cities in Nevada are hiring for Research Machine Learning Federated Learning jobs? Cities in Nevada with the most Research Machine Learning Federated Learning job openings:
Research Machine Learning Federated Learning jobs near you

Senior Machine Learning Engineer

TensorWave

Las Vegas, NV • On-site, Remote

$117K - $154K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 4 days ago

Job description

About TensorWave
Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure.
About the Role
We're looking for a Senior Machine Learning Engineer to join our team during an exciting phase of growth. In this role, you'll be responsible for building and operating the core systems that power large-scale ML training and inference across TensorWave's GPU platform, working closely with cross-functional partners to support business objectives while upholding our standards for excellence, collaboration, and impact.
What You'll Do
  • Design, operate, and improve ML infrastructure systems supporting distributed training and inference workloads
  • Build reliable, repeatable workload execution and orchestration patterns across shared GPU environments
  • Troubleshoot performance, reliability, and scalability issues across the ML stack
  • Partner with ML, systems, and platform teams to improve developer experience and operational efficiency

Who You Are
Required Qualifications
  • Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
  • Expertise supporting production ML systems using SLURM and Kubernetes
  • Strong understanding of GPU-accelerated workloads and distributed systems concepts
  • Solid Linux fundamentals and experience debugging infrastructure-level issues
  • Ability to build automation and tooling - Python, Go, etc.

Preferred Qualifications
  • Experience working across schedulers, orchestration platforms, or cluster managers
  • Familiarity with large-scale GPU environments or HPC-style systems
  • Experience improving infrastructure reliability, utilization, or performance at scale

What We Offer
  • Stock Options
  • 100% paid Medical, Dental, and Vision insurance for Employees
  • Company Health Savings Account Contributions
  • 100% paid Short Term and Long Term Disability Insurance for Employees
  • Life and Voluntary Supplemental Insurance Options
  • Other Insurance Options, such as Pet & Legal Insurance
  • Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support
  • Flexible Spending Account
  • 401(k)
  • Employee Assistance Program
  • Flexible PTO
  • Paid Holidays
  • Parental Leave
  • Other In-Office Perks

Equal Employment Opportunity
TensorWave is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of any protected status under applicable law.
Reasonable Accommodations
TensorWave provides reasonable accommodations in accordance with applicable laws. If you require accommodation during the hiring process, please contact accomodations@tensorwave.com.
Employment Eligibility
All offers of employment are contingent upon verification of identity and authorization to work in the United States, as required by law.
Background Checks
Where permitted by law, employment may be contingent upon the successful completion of a job-related background check.
Data Privacy Notice
By submitting an application, you acknowledge that TensorWave may collect, use, and retain your personal information for recruiting and employment-related purposes in accordance with applicable data privacy laws.

Apply