1

Freelance Nvidia Machine Learning Jobs (NOW HIRING)

Technical Architect - Machine Learning

$67.75 - $82/hr

We have been recognized with: * 3x AWS AI/ML award wins. * 3x NVIDIA Partner of the Year title ... Role: Architect - Machine Learning Experience Level: 7+ years Employment type: Full Time Location:

Technical Architect - Machine Learning

OR · Remote

$66.25 - $80/hr

We have been recognized with: * 3x AWS AI/ML award wins. * 3x NVIDIA Partner of the Year title ... Role: Architect - Machine Learning Experience Level: 7+ years Employment type: Full Time Location:

Senior Software Engineer, AI Networking

Seattle, WA

$139.40K - $183.80K/yr

NVIDIA seeks a senior software engineer to join the AI Networking co-design and benchmark R&D team. In this pivotal role, the candidate is responsible for building and productizing machine learning ...

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA ...

OR · Hybrid

We are now looking for a Senior Machine Learning Applications and Compiler Engineer ... NVIDIA is seeking engineers to develop algorithms and optimizations for our LPX inference and ...

What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ... We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA ...

Senior Software Engineer, AI Networking

Santa Clara, CA · On-site

$143.90K - $189.70K/yr

NVIDIA seeks a senior software engineer to join the AI Networking co-design and benchmark R&D team. In this pivotal role, the candidate is responsible for building and productizing machine learning ...

next page

Showing results 1-20

Freelance Nvidia Machine Learning information

See salary details

$14

$47

$132

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

As of May 30, 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 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.

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.

More about Freelance Nvidia Machine Learning jobs
What cities are hiring for Freelance Nvidia Machine Learning jobs? Cities with the most Freelance Nvidia Machine Learning job openings:
What are the most commonly searched types of Nvidia Machine Learning jobs? The most popular types of Nvidia Machine Learning jobs are:
What states have the most Freelance Nvidia Machine Learning jobs? States with the most job openings for Freelance Nvidia Machine Learning jobs include:
Infographic showing various Freelance Nvidia Machine Learning job openings in the United States as of May 2026, with employment types broken down into 98% Full Time, 1% Temporary, and 1% Nights. Highlights an 91% Physical, and 9% Hybrid job distribution, with an average salary of $99,230 per year, or $47.7 per hour.

Physicist/Scientist Machine Learning

Amat

Santa Clara, CA • On-site, Remote

$138K - $190K/yr

Full-time

Posted 11 days ago


Job description

Who We Are

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips - the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world - like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.

What We Offer

Salary:

$138,000.00 - $190,000.00

Location:

Santa Clara,CA

You'll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible-while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We're committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits.

We are seeking a highly motivated MS or PhDlevel scientist or engineer to develop and apply machine learning-based models using data generated from multidimensional, highperformance computing (HPC) simulations. The successful candidate will work at the intersection of physicsbased modeling, largescale simulation, and modern AI/ML methods to accelerate product developing in the fast-paced semiconductor equipment industry. Focus will be on developing ML models based on plasma and electromagnetic simulations.

This role is ideal for candidates with strong domain knowledge in engineering or physical sciences and handson experience translating complex simulation data into robust, predictive machine learning models.

Required Qualifications

  • MS or PhD in Engineering (e.g., Chemical, Electrical, Mechanical, Aerospace, Nuclear, Materials), Science (e.g., Physics, Chemistry), or Computer Science
  • Significant experience developing machine learning or deep learning models using data from multidimensional numerical simulations (e.g., PDEbased solvers, particlebased simulations, multiphysics models)
  • Strong background in Pythonbased scientific computing and ML workflows
  • Demonstrated experience with PyTorch or equivalent deep learning frameworks
  • Solid understanding of:
    • Data preprocessing and feature engineering for large, highdimensional datasets
    • Model training, validation, and performance evaluation
    • Numerical methods and/or physicsbased modeling concepts

Preferred Qualifications

  • Experience with NVIDIA Physics NeMo, NVIDIA Modulus, or related physicsinformed or simulationdriven ML libraries
  • Familiarity with GPUaccelerated computing, CUDAaware workflows, and HPC environments
  • Exposure to physicsinformed machine learning (PIML), surrogate modeling, reducedorder modeling, or operator learning
  • Publications or demonstrated research contributions in ML for physical systems or related fields

Key Responsibilities

  • Develop and train machine learning and deep learning models using data from largescale, multidimensional HPC simulations
  • Collaborate with domain experts to incorporate physical constraints, scientific insight, and prior knowledge into ML model design
  • Design workflows for data ingestion, curation, and analysis of highvolume simulation outputs
  • Evaluate model accuracy, generalization, and robustness across a wide range of operating conditions
  • Optimize models for performance, scalability, and deployment on GPUaccelerated platforms
  • Contribute to internal software tools, modeling frameworks, and best practices

Additional Information

Time Type:

Full time

Employee Type:

New College Grad

Travel:

Yes, 10% of the Time

Relocation Eligible:

Yes

The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.

For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.

Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.

In addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at Accommodations_Program@amat.com, or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.