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Freelance Nvidia Machine Learning Jobs (NOW HIRING)

Nvidia

Senior Machine Learning Engineer

Santa Clara, CA

$143K - $189K/yr

Intelligent machines powered by Artificial Intelligence computers that can learn, reason, and ... NVIDIA's GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of ...

Machina Labs

Machine Learning Engineer

Chatsworth, CA · On-site

$160K - $190K/yr

Backed by Lockheed Martin, Toyota, and NVIDIA, we're building the manufacturing infrastructure that ... We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of ...

Machina Labs

Machine Learning Engineer

Los Angeles, CA

$160K - $190K/yr

Backed by Lockheed Martin, Toyota, and NVIDIA, we're building the manufacturing infrastructure that ... We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of ...

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How much do freelance nvidia machine learning jobs pay per hour?

As of Jul 9, 2026, the average hourly pay for freelance nvidia machine learning in the United States is $47.71, according to ZipRecruiter salary data. Most workers in this role earn between $24.28 and $61.78 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Freelance Nvidia Machine Learning Engineer, and why are they important?

To thrive as a Freelance Nvidia Machine Learning Engineer, you need a strong background in machine learning principles, deep learning frameworks (such as TensorFlow or PyTorch), and proficiency in Python programming, often supported by a relevant degree or certifications. Familiarity with Nvidia hardware (GPUs), CUDA programming, and tools like Nvidia Deep Learning SDKs is essential for optimizing and deploying models efficiently. Exceptional problem-solving, self-management, and client communication skills help you deliver effective solutions and maintain successful freelance relationships. Mastery of these skills ensures you can build high-performance models, meet client expectations, and stay competitive in the rapidly evolving ML landscape.

What is the difference between Freelance Nvidia Machine Learning vs Freelance Data Scientist?

AspectFreelance Nvidia Machine LearningFreelance Data Scientist
Required CredentialsKnowledge of Nvidia GPU architectures, CUDA programming, machine learning frameworksStatistics, programming, data analysis skills, often with similar certifications
Work EnvironmentProject-based, remote, often with tech companies or startupsProject-based or consulting, remote or on-site, across various industries
Industry UsageAI, deep learning, GPU-accelerated applicationsData analysis, predictive modeling, business insights

Freelance Nvidia Machine Learning specialists focus on GPU-accelerated AI projects using Nvidia technologies, while Freelance Data Scientists handle broader data analysis and modeling tasks. Both roles are in high demand for tech-driven projects but differ in technical focus and tools used.

What are some common challenges freelance Nvidia Machine Learning specialists face when working with clients remotely?

Freelance Nvidia Machine Learning specialists often encounter challenges such as ensuring compatibility between client hardware and Nvidia GPU requirements, effectively communicating technical needs and project progress to non-expert clients, and managing project timelines without in-person oversight. Additionally, freelancers may need to set up secure access to client data or cloud environments, which can require extra coordination. Proactively clarifying expectations, maintaining clear documentation, and staying current with Nvidia's latest tools (like CUDA, cuDNN, or TensorRT) are essential strategies for overcoming these challenges.

What does a Freelance Nvidia Machine Learning specialist do?

A Freelance Nvidia Machine Learning specialist is an independent contractor who uses Nvidia hardware and software platforms, such as CUDA and TensorRT, to develop, optimize, and deploy machine learning models. These professionals often work with clients to accelerate AI workloads, implement deep learning solutions, and leverage GPU computing for data processing tasks. Their projects may include computer vision, natural language processing, or other AI applications that benefit from Nvidia’s technology stack. Freelancers in this field need strong programming skills, familiarity with Nvidia SDKs, and experience optimizing models for high-performance computing environments.
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Freelance Nvidia Machine Learning jobs near you
Infographic showing various Freelance Nvidia Machine Learning job openings in the United States as of July 2026, with employment types broken down into 2% Locum Tenens, 20% Full Time, 37% Part Time, 20% Contract, 17% Nights, and 4% Summer. Highlights an 83% Physical, 1% Hybrid, and 16% Remote job distribution, with an average salary of $99,230 per year, or $47.7 per hour.
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Nvidia

Santa Clara, CA

$143K - $189K/yr

Full-time

Posted 4 days ago

Job description

Intelligent machines powered by Artificial Intelligence computers that can learn, reason, and interact with people are no longer science fiction. Today, a self-driving vehicle powered by AI can meander through a country road at night and find its way. NVIDIA's GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots, and self-driving vehicles that can perceive and understand the world.

Our team is building the machine learning backbone of the Perception component for NVIDIA DRIVE AV. We are seeking the best Machine Learning Engineers with a background in computer vision, LiDAR & camera perception, and AI infrastructure who are passionate about solving the hardest problems for self-driving cars. Are you interested in inventing human-level AI for perception in the unconstrained world under any conditions? If so, join us!

What You'll Be Doing:

  • Model Development: Design, train, and optimize innovative machine learning models for LiDAR perception (e.g., road element detection, semantic segmentation, tracking).

  • Develop and coordinate entire ML workflows, covering data pipelines, model training, model metrics, continuous performance instrumentation, and reporting.

  • Productization: Take ML models and algorithms from initial evaluation and experimentation all the way to shipping them as part of the NVIDIA DRIVE AV platform, developing highly efficient product code in C++.

  • Innovation: Keep track of the latest developments in machine learning, and incorporate techniques that improve platform performance.

  • Collaborate with LiDAR/camera teams, developers, engineers, and managers to turn complex ideas into reliable solutions for autonomous driving.

What We Need to See:

  • BS or MS in Computer Science, Engineering, or a related field, or equivalent experience.

  • 6+ years of relevant proven industry experience applying machine learning to address real-world problems.

  • Strong C++ and Python programming and debugging skills with experience in developing for large, complex systems.

  • Deep practical experience applying machine learning to lidar/camera perception in automotive or related fields.

  • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and a strong understanding of the mathematical foundations of ML.

  • Building and sustaining training and essential metric workflows for large-scale datasets.

  • Excellent communication and analytical skills. Self-motivated drive to solve hard problems.

Ways to Stand Out From the Crowd:

  • LiDAR or Camera Perception Experience: Proven track record of developing and shipping deep learning models for LiDAR/Camera in a production environment.

  • Advanced Model Knowledge: Familiarity with modern network architectures like Transformers and their application to visual recognition tasks.

  • AV Production Experience: A history of delivering ML features and models into a production autonomous vehicle stack or a related robotics product.

  • Performance Optimization: Experience with model optimization for real-time inference on embedded or automotive platforms (e.g., using TensorRT).

We believe that realizing self-driving vehicles will be a defining contribution of our generation (e.g., traffic accidents are responsible for ~1.25 million deaths per year worldwide). We have the funding and scale, but we need your help on our team. NVIDIA is widely considered to be one of the technology world's most desirable employers with some of the most brilliant and talented people in the world working here. If you're creative and autonomous, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 9, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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