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Freelance Nvidia Machine Learning Jobs (NOW HIRING)

## Senior Machine Learning Applications and Compiler Engineer, LPXApplylocations: US, CA, Santa Clara ... NVIDIA is seeking engineers to develop algorithms and optimizations for our LPX inference and ...

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 ...

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 ...

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

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 Sep 11, 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 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 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 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 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.

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Infographic showing various Freelance Nvidia Machine Learning job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $99,230 per year, or $47.7 per hour.

AI & Machine Learning Engineer

Northbrook, IL • On-site

Other

Re-posted 21 days ago


Job description

About LightSpeed

LightSpeed Build Technologies is revolutionizing the construction industry through AI-powered robotics. Our flagship systems—BRUTE for automated wall panel manufacturing and DEX for on-site collaborative construction—are addressing the global housing crisis by delivering unprecedented speed, precision, and affordability in homebuilding.

Position Overview:

As an AI & Machine Learning Engineer, you will design, build, and deploy the intelligent systems that make LightSpeed’s construction robots smarter, faster, and more autonomous. You will develop machine learning models for computer vision, predictive analytics, autonomous decision‑making, and process optimization—all deployed in real‑time production environments where precision and reliability are critical. This role sits at the intersection of cutting‑edge AI research and practical industrial application.

What you'll work on:

Machine Learning Development

  • 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

Computer Vision & Perception

  • 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

Data Infrastructure & MLOps

  • 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)

Integration & Deployment

  • 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

Required Qualifications:

AI/ML Expertise

  • 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)

Software Engineering

  • 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 Qualifications:
  • 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

Why Join LightSpeed:
  • Build AI systems that directly control physical robots addressing the housing crisis

  • Work on rare real‑world ML challenges: real‑time inference, embodied AI, industrial perception

  • Access to rich proprietary datasets from production robot cells

  • Hands‑on culture with direct access to robots, sensors, and manufacturing environments

  • Competitive compensation including salary, equity, and comprehensive benefits

Employment Relationship

This position is at‑will, meaning either you or the Company may terminate employment at any time, with or without cause or notice.

Equal Opportunity

LightSpeed Build Technologies is an equal opportunity employer committed to building a diverse and inclusive workplace. We welcome candidates from all backgrounds and experiences.

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