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Freelance Nvidia Machine Learning Jobs in Phoenix, AZ

Prosum Inc.

Sr. Machine Learning Engineer

Phoenix, AZ · On-site

$130K - $150K/yr

Evaluate and implement performance improvements using NVIDIA GPU technologies and profiling tools. Model Deployment & Optimization * Optimize, quantize, and deploy machine learning models using ...

Honeywell International, Inc.

Lead AI Engineer

Phoenix, AZ · On-site

$96K - $126K/yr

... with NVIDIA's physics-accelerated AI tools such as Physics NEMO, Modulus, Warp, or similar platforms for physics-informed deep learning We Value • Proficiency in Python and machine learning ...

Deloitte

Oracle Gen AI Manager

Tempe, AZ

... machine learning initiatives * Identify high-value AI use cases and guide teams on prompt ... Experience with LangChain, LangGraph, NVIDIA NIM, or Hugging Face * Experience leading AI or ERP ...

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Freelance Nvidia Machine Learning information

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$14

$47

$131

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 Phoenix, AZ is $47.37, according to ZipRecruiter salary data. Most workers in this role earn between $24.09 and $61.35 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 Phoenix, AZ?

The most popular types of Nvidia Machine Learning jobs in Phoenix, AZ are:

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

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Freelance Nvidia Machine Learning jobs near you
Infographic showing various Freelance Nvidia Machine Learning job openings in Phoenix, AZ as of June 2026, with employment types broken down into 9% As Needed, 50% Full Time, 13% Part Time, 9% Temporary, 16% Contract, and 3% Nights. Highlights an 89% Physical, 5% Hybrid, and 6% Remote job distribution, with an average salary of $98,527 per year, or $47.4 per hour.

Sr. Machine Learning Engineer

Prosum Inc.

Phoenix, AZ • On-site

$130K - $150K/yr

Other

Posted 9 days ago

Job description

Job Description
Sr. Machine Learning Engineer
Salary Range: $130k to $150k
Our client is seeking a Sr. Machine Learning Engineering for a direct hire role to sit in North Phoenix, AZ or Hillsboro, OR. This role will be onsite 4 days a week and 1 remote day.
JOB SUMMARY
The role of Senior Machine Learning Engineer will architect and optimize real-time, high-throughput, and ultra-low latency image pipelines for next-generation Mask Inspection Tools. Responsibilities include eliminating hardware bottlenecks through CUDA kernel tuning and GPU parallel computing, ensuring deep learning models and CV algorithms seamlessly processing massive, high-bandwidth streaming data at production scale.
ESSENTIAL DUTIES AND RESPONSIBILITIES
High-Performance Computing Pipeline Architecture
  • Design, implement, and optimize high-throughput, low-latency image processing pipelines for real-time optical inspection and machine vision systems.
  • Develop scalable architectures capable of processing large volumes of imaging data while meeting stringent latency and reliability requirements.
  • Profile and optimize system performance across CPU, GPU, memory, and I/O subsystems.
GPU Acceleration
  • Design, develop, and optimize CUDA kernels to accelerate deep learning inference and classical computer vision algorithms.
  • Maximize GPU utilization through efficient memory management, kernel optimization, and parallel programming techniques.
  • Evaluate and implement performance improvements using NVIDIA GPU technologies and profiling tools.
Model Deployment & Optimization
  • Optimize, quantize, and deploy machine learning models using TensorRT, ONNX Runtime, or similar inference frameworks.
  • Integrate AI models into production-grade C++ and Python applications.
  • Improve inference throughput, latency, and resource utilization while maintaining model accuracy.
  • Develop automated deployment and validation pipelines for machine learning models.
Concurrency & Systems Optimization
  • Architect and implement multi-threaded, high-concurrency software components for data acquisition, buffering, streaming, and real-time processing.
  • Design robust synchronization and communication mechanisms between hardware interfaces and AI processing pipelines.
  • Optimize end-to-end system performance for deterministic, real-time execution.
Cross-Functional Collaboration
  • Partner with machine learning scientists, computer vision engineers, hardware engineers, and software developers to deliver integrated AI solutions.

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