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Research Assistant Machine Learning Jobs in Nevada

Motional

Machine Learning Systems Engineer

Las Vegas, NV · On-site +1

You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and ...

Motional

Machine Learning Systems Engineer

Las Vegas, NV · On-site +1

$144K - $192K/yr

You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and ...

Motional

Machine Learning Systems Engineer

Las Vegas, NV · On-site

$144K - $192K/yr

You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and ...

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How much do research assistant machine learning jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for research assistant machine learning in Nevada is $22.31, according to ZipRecruiter salary data. Most workers in this role earn between $18.85 and $25.96 per hour, depending on experience, location, and employer.

What is a Research Assistant Machine Learning job?

A Research Assistant in Machine Learning supports research projects by implementing algorithms, analyzing data, and conducting experiments to advance AI models. They assist senior researchers by preprocessing datasets, developing machine learning models, and evaluating their performance. Responsibilities may also include coding, literature reviews, and writing research papers. This role is typically found in academia, research labs, or industry R&D teams. Strong programming skills, statistical knowledge, and familiarity with ML frameworks like TensorFlow or PyTorch are essential.

What are the key skills and qualifications needed to thrive in the Research Assistant Machine Learning position, and why are they important?

To thrive as a Research Assistant Machine Learning, you need a solid understanding of machine learning algorithms, programming skills (especially in Python or R), and a background in statistics or computer science, often supported by a bachelor’s or master’s degree. Experience with frameworks such as TensorFlow, PyTorch, and data analysis tools, as well as familiarity with version control systems like Git, is highly beneficial. Strong problem-solving abilities, attention to detail, and effective communication skills help you excel in collaborative research environments. These skills ensure you can contribute meaningfully to research projects, analyze complex datasets, and communicate findings effectively within interdisciplinary teams.

What types of projects might a Research Assistant in Machine Learning typically work on?

As a Research Assistant in Machine Learning, you may be involved in projects such as developing and evaluating predictive models, processing and analyzing large datasets, and assisting in the publication of research findings. Your work could contribute to applications like natural language processing, computer vision, or recommendation systems, depending on the focus of the research group. You’ll often collaborate closely with senior researchers, data scientists, or PhD students, allowing you to participate in brainstorming sessions, code development, and experimental design. This experience provides valuable exposure to cutting-edge technology and can serve as a strong foundation for a research or industry career in machine learning.
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Research Assistant Machine Learning jobs near you
Infographic showing various Research Assistant Machine Learning job openings in Nevada as of May 2026, with employment types broken down into 1% As Needed, 89% Full Time, 6% Part Time, 1% Temporary, and 3% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution, with an average salary of $46,405 per year, or $22.3 per hour.
Machine Learning Systems Engineer

Machine Learning Systems Engineer

Motional

Las Vegas, NV • On-site, Remote

Other

Posted 23 days ago

Job description

Mission Summary:

We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at scale, focusing obsessively on speed, cost, reliability, and throughput. You will work at the intersection of machine learning research and high-performance systems engineering. Your work will directly impact our ability to scale large-scale distributed model training and reduce the time-to-convergence for our next generation of models.

What you'll be doing:

  • Performance Profiling & Optimization: Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time.
  • Distributed Training: Optimize distributed training pipelines using frameworks such as PyTorch Distributed.
  • Kernel Development: Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.
  • Data Pipeline Engineering: Optimize robust data loading pipelines that maximize training throughput.

What we're looking for:

  • Education: Bachelor's, Master's degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline.
  • Software Engineering: Strong proficiency in Python.
  • ML Frameworks: Extensive hands-on experience with PyTorch.
  • ML Knowledge: Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes.
  • Problem Solving: Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges.

We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote.

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