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Freelance Nvidia Machine Learning Jobs in Seattle, WA

Nvidia

Principal Graphics Developer Tools Engineer

Seattle, WA · On-site

$159K - $196K/yr

... machine learning or generative AI techniques to software development workflows. * Contributions to game engines, graphics middleware, graphics SDKs, or open-source graphics projects. NVIDIA is widely ...

Nvidia

Senior Full-Stack Lead Engineer

Seattle, WA

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than ... Experience building machine learning platforms or self-service internal infrastructure tools ...

Nvidia

Senior GPU Supercomputer Scheduler Engineer

Redmond, WA · On-site

$137K - $180K/yr

... in machine learning, cloud computing, and system co-design. What you'll be doing: * Design and ... NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive ...

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Freelance Nvidia Machine Learning information

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$16

$54

$150

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

As of Aug 15, 2026, the average hourly pay for freelance nvidia machine learning in Seattle, WA is $54.29, according to ZipRecruiter salary data. Most workers in this role earn between $27.64 and $70.29 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a freelance Nvidia machine learning specialist?

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.

What are the most commonly searched types of Nvidia Machine Learning jobs in Seattle, WA?

The most popular types of Nvidia Machine Learning jobs in Seattle, WA are:

What are popular job titles related to Freelance Nvidia Machine Learning jobs in Seattle, WA?

For Freelance Nvidia Machine Learning jobs in Seattle, WA, the most frequently searched job titles are:

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The top searched job categories for Freelance Nvidia Machine Learning jobs in Seattle, WA are:

Freelance Nvidia Machine Learning jobs near you

Senior AI Performance and Efficiency Engineer

Nvidia

Seattle, WA • On-site

$118K - $163K/yr

Full-time

Re-posted 14 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

7th of 244 rated software companies

Job description

We are seeking a Senior AI/ML Performance and Efficiency Engineer, GPU Clusters at NVIDIA to join our AI Efficiency efforts. As an Engineer, you will have a pivotal role in enhancing efficiency for our researchers by implementing progressions throughout the entire stack. Your main task will revolve around collaborating closely with customers to pinpoint and address infrastructure and application deficiencies, facilitating groundbreaking AI and ML research on GPU Clusters. Together, we can craft potent, effective, and scalable solutions as we mold the future of AI/ML technology!

What you will be doing:

  • Collaborate closely with our AI/ML researchers to make their ML models more efficient leading to significant productivity improvements and cost savings

  • Build tools, frameworks, and apply ML techniques to detect & analyze efficiency bottlenecks and deliver productivity improvements for our researchers

  • Work with researchers working on a variety of innovative ML workloads across Robotics, Autonomous vehicles, LLM's, Videos and more

  • Collaborate across the engineering organizations to deliver efficiency in our usage of hardware, software, and infrastructure

  • Proactively monitor fleet wide utilization patterns, analyze existing inefficiency patterns, or discover new patterns, and deliver scalable solutions to solve them

  • Keep up to date with the most recent developments in AI/ML technologies, frameworks, and successful strategies, and advocate for their integration within the organization.

What we need to see:

  • BS or similar background in Computer Science or related area (or equivalent experience)

  • Minimum 5+ years of experience designing and operating large scale compute infrastructure

  • Strong understanding of modern ML techniques and tools

  • Experience investigating, and resolving, training & inference performance end to end

  • Debugging and optimization experience with NSight Systems and NSight Compute

  • Experience with debugging large-scale distributed training using NCCL

  • Proficiency in programming & scripting languages such as Python, Go, Bash, as well as familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) in addition to experience with parallel computing frameworks and paradigms.

  • Dedication to ongoing learning and staying updated on new technologies and innovative methods in the AI/ML infrastructure sector.

  • Excellent communication and collaboration skills, with the ability to work effectively with teams and individuals of different backgrounds

Ways to stand out from the crowd:

  • Background with NVIDIA GPUs, CUDA Programming, NCCL and MLPerf benchmarking

  • Experience with Machine Learning and Deep Learning concepts, algorithms and models

  • Familiarity with InfiniBand with IBOP and RDMA

  • Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads

  • Familiarity with deep learning frameworks like PyTorch and TensorFlow

NVIDIA offers competitive salaries and a comprehensive benefits package. Our engineering teams are growing rapidly due to outstanding expansion. If you're a passionate and independent engineer with a love for technology, 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 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until March 23, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse 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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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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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