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Freelance Nvidia Machine Learning Jobs in Chicago, IL

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... NVIDIA DGX Spark. Understanding of FDA regulatory requirements for AI/ML in medical devices ...

Deloitte

Oracle Gen AI Manager

Chicago, IL · On-site

... 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 ...

Deloitte

Oracle Gen AI Manager

Chicago, IL · On-site

... 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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All JobsNvidia Machine Learning JobsFreelance Nvidia Machine Learning JobsFreelance Nvidia Machine Learning Jobs in Chicago, IL

Freelance Nvidia Machine Learning information

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$15

$49

$136

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

As of Aug 1, 2026, the average hourly pay for freelance nvidia machine learning in Chicago, IL is $49.15, according to ZipRecruiter salary data. Most workers in this role earn between $25.00 and $63.65 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

AI & Machine Learning Engineer

Lightspeed

Northbrook, IL • On-site

Full-time

Posted 10 days ago

Job description

Job Summary:
LightSpeed Build Technologies is revolutionizing the construction industry through AI-powered robotics. As an AI & Machine Learning Engineer, you will design, build, and deploy intelligent systems for construction robots, focusing on machine learning models for computer vision, predictive analytics, and process optimization.
Responsibilities:
• Design, train, and deploy ML models for robotic control, quality prediction, and process optimization
• Develop reinforcement learning and imitation learning systems for robot task planning
• Build predictive maintenance models using sensor data to anticipate equipment failures
• Implement anomaly detection for real-time quality monitoring during automated assembly
• Optimize model inference for edge deployment on GPU-accelerated hardware in production
• Develop deep learning pipelines for object detection, segmentation, and pose estimation
• Build real-time vision systems for robotic guidance, workpiece tracking, and dimensional verification
• Implement 3D point cloud processing for construction material recognition
• Design and train models for visual quality inspection using depth cameras and industrial imaging
• Build ML data pipelines from sensor acquisition through model training and deployment
• Establish data labeling, versioning, and management workflows for training datasets
• Implement model monitoring, A/B testing, and continuous improvement in production
• Design experiment tracking and reproducibility infrastructure (MLflow, Weights & Biases)
• Integrate ML models with ROS2-based robot control for real-time inference
• Optimize models for NVIDIA Jetson, industrial PCs, and edge computing platforms
• Collaborate with robotics engineers on sensor selection, placement, and calibration
• Support scaling ML systems across multiple production cells and sites
Qualifications:
Required:
• 4+ years hands-on ML engineering building and deploying production models
• Deep proficiency with PyTorch or TensorFlow for model development and training
• Strong computer vision experience: object detection, segmentation, depth estimation, or 3D vision
• Understanding of reinforcement learning, imitation learning, or robot learning approaches
• Experience optimizing ML models for edge deployment (TensorRT, ONNX, quantization)
• Strong Python with experience in C++ for performance-critical components
• Experience with ML infrastructure: data pipelines, experiment tracking, model serving
• Proficiency with Linux, Docker, Git, and CI/CD workflows
• Understanding of real-time system constraints for ML inference in production
Preferred:
• MS or PhD in Machine Learning, Computer Science, Robotics, or related field
• Experience with robotics simulation: MuJoCo, IsaacSIM, or similar
• Background in manufacturing, industrial automation, or construction technology
• Experience with ROS/ROS2 integration for ML-powered robotics
• Published research or patents in computer vision, robot learning, or related ML
• Experience with NVIDIA ecosystem: CUDA, cuDNN, TensorRT, Jetson platforms
Company:
BUILDING TOMORROW'S HOMES, FASTER AND SMARTER The Lightspeed Integrated Walls, Floors and Roof Systems are built with advanced software and AI driven industrial robots, allowing us to seamlessly craft the walls, floors and roofs, integrating the framing, MEPs, insulation, and drywall in a single, efficient manufacturing line. Founded in , the company is headquartered in , , with a team of 11-50 employees. The company is currently Early Stage.

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