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Freelance Nvidia Machine Learning Jobs (NOW HIRING)

Nvidia

Senior Deep Learning Software Engineer

Santa Clara, CA · Hybrid

$143K - $189K/yr

Familiarity with NVIDIA's deep learning SDKs such as TensorRT. * Prior experience in writing high-performance GPU kernels for machine learning workloads in frameworks such as CUDA, CUTLASS, or Triton.

Nvidia

Senior Deep Learning Software Engineer

Redmond, WA · Hybrid

$137K - $180K/yr

Familiarity with NVIDIA's deep learning SDKs such as TensorRT. * Prior experience in writing high-performance GPU kernels for machine learning workloads in frameworks such as CUDA, CUTLASS, or Triton.

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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.

Deep Learning Product Research Engineer

NVIDIA AI

Santa Clara, CA • On-site

Full-time

Posted 8 days ago

Job description

Job Summary:
NVIDIA AI is at the forefront of the AI revolution, focusing on deep learning platforms and technologies. The Deep Learning Product Research Engineer will lead product research for generative AI, build prototypes, and collaborate with various teams to enhance NVIDIA’s AI ecosystem.
Responsibilities:
• Lead product research for generative AI by evaluating emerging models, agent technology, reinforcement learning, and evaluation methods, then assessing what they mean for NVIDIA products.
• Build proof-of-concept applications, benchmarks, and reference sample code that validate new capabilities and demonstrate product value.
• Convert customer, developer, benchmark, usage, and field signals into structured product intelligence, including adoption trends, friction points, issue reproductions, and roadmap recommendations.
• Develop enterprise-ready enablement assets such as reference architectures, integration playbooks, performance tuning recipes, and demo-to-production workflows for Nemotron, NeMo, NIM, and related NVIDIA AI software.
• Partner with research, engineering, product management, technical marketing, field teams, and customers to turn insights into feature requests, launch inputs, positioning, and usability improvements.
• Advance internal LLM expertise and tooling through reusable evaluation harnesses, profiling utilities, agentic workflows, and practical analysis of model behavior.
• Distill hands-on research and engineering work into authoritative technical assets, including code examples, technical write-ups, white papers, demos, talks, and patents where appropriate.
• Stay current with advances in model training, post-training, inference, agentic systems, evaluation, deployment, safety, and the broader AI developer ecosystem.
Qualifications:
Required:
• Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent experience.
• 5+ years of proven experience in software engineering, machine learning engineering, AI engineering, solutions architecture, applied research, or a similar technical role.
• Hands-on experience with machine learning, deep learning, or agentic AI, including building, training, fine-tuning, evaluating, deploying, or optimizing models and AI applications.
• Practical experience with generative AI systems, including large language models, retrieval-augmented generation, agentic workflows, model evaluation, or AI application development.
• Experience with Python and modern deep learning frameworks and libraries such as PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, TensorFlow, or similar tools.
• Familiarity with modern AI-assisted development tools and coding agents such as Codex, Claude Code, Cursor, or similar systems.
• Ability to create clear, accurate, technically rigorous, and compelling content for developers, including tutorials, blogs, sample code, white papers, benchmarks, or demos.
• Strong communication and presentation skills, with the ability to explain complex technical topics to both expert and non-expert audiences.
Preferred:
• PhD in Computer Science, Engineering, Machine Learning, Artificial Intelligence, or a related field.
• 3+ years of hands-on experience with machine learning, deep learning, generative AI, large language models, multimodal models, reinforcement learning, model optimization, or agentic applications.
• Experience designing or evaluating agentic AI systems, AI coding assistants, model evaluation harnesses, RAG pipelines, synthetic data workflows, or AI safety workflows.
• Experience with NVIDIA AI software, models, or frameworks such as NeMo, NeMo Retriever, NeMo Guardrails, NeMo RL, NIM, TensorRT, Dynamo, CUDA, cuDNN, or Nemotron models.
• Familiarity with the broader generative AI ecosystem, including open models, agent frameworks, vector databases, evaluation tools, deployment platforms, and emerging AI developer workflows.
Company:
Explore the latest breakthroughs made possible with AI. Founded in , the company is headquartered in Santa Clara, CA, US, , with a team of 10001+ employees. The company is currently Late Stage.

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