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Freelance Nvidia Machine Learning Jobs (NOW HIRING)

Quantiphi, Inc.

Senior Machine Learning Engineer

$125K - $165K/yr

Industry Verticals (Telco, BFSI, HCLS etc.) and is an established Elite/Premier Partner of NVIDIA ... Role: Senior Machine Learning Engineer Experience Level: 3-6+ yrs Work Location: Dallas, TX Role ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

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 ...

Nvidia

Senior Software Engineer, AI Networking

Seattle, WA

$139K - $183K/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 ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

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 ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

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 ...

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Freelance Nvidia Machine Learning information

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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.
Real Time Machine Learning (PaaS)

Real Time Machine Learning (PaaS)

Ruri Software Technologies LLC

Irving, TX • On-site

Full-time

Posted 20 days ago

Job description

Job Title: Real Time Machine Learning (PaaS)
Duration: 8+ months
Work Location: Irving TX
Job Description:
  • Strong experience in Kubernetes and distributed computing for real time Al applications (CKAD, CKA certification preferred)
  • ⁠Experience with advanced ML, DL frameworks including TF,Pytorch,Ray
  • ⁠Hands on Experience with CUDA/NVIDIA ecosystem
  • ⁠Exposure to Real time monitoring frameworks including but not limited to Prometheus, Grafana, Open Telemetry (ML Democratization)
  • ⁠Strong experience working with Model Development, ML pipeline at scale with distributed computing technologies lik Pyspark, Ray
  • Ability to work on Distributed training pipelines and be aware of end to end CICD process for Al/ML workloads
  • Orchestrate the model scoring pipelines using Cloud Composer
    Deploy Auto-ML solutions in the production systems
  • ⁠Awareness and hands-on Data Lake, Big data ecosystems
  • ⁠Exposure to containers, cloud native solutioning

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