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Hourly Embedded Machine Learning Jobs in California

Nvidia

Senior Machine Learning Engineer

Santa Clara, CA · On-site

$143K - $189K/yr

We are seeking the best Machine Learning Engineers with a background in computer vision, LiDAR ... Experience with model optimization for real-time inference on embedded or automotive platforms (e.g ...

Nvidia

Senior Machine Learning Engineer

Santa Clara, CA

$143K - $189K/yr

We are seeking the best Machine Learning Engineers with a background in computer vision, LiDAR ... Experience with model optimization for real-time inference on embedded or automotive platforms (e.g ...

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA · On-site

$140K - $170K/yr

Implement physiological parameter measurement algorithms on embedded systems * Deliver high quality ... Algorithm development experience using machine learning techniques such as regression ...

Apple

Embedded ISP Engineer

San Diego, CA · On-site

$142K - $263K/yr

... and machine learning techniques, all while contributing to the future of photography and ... Build drivers for advanced image processing pipelines in embedded systems, working with the latest ...

Masimo

Sr Engineer, AI/Machine Learning

Irvine, CA · On-site

$140K - $170K/yr

Implement physiological parameter measurement algorithms on embedded systems * Deliver high quality ... Algorithm development experience using machine learning techniques such as regression ...

Apple

Camera Embedded Software Engineer

Cupertino, CA · On-site

$162K - $213K/yr

As part of the team you would work on core camera/ISP/Machine learning technologies, including ... 3+ years of embedded software development experience Proficiency in C/C++ Proficiency in ...

Apple

Camera Embedded Software Engineer

Cupertino, CA · On-site

$162K - $213K/yr

As part of the team you would work on core camera/ISP/Machine learning technologies, including ... 10+ years of embedded software development experience Proficiency in C/C++ Proficiency in ...

Apple

Camera Embedded Software Engineer

Cupertino, CA

$216K - $324K/yr

As part of the team you would work on core camera/ISP/Machine learning technologies, including ... Design and implement camera features in embedded systems for Apple products. You will work with ...

Apple

Camera Embedded Software Engineer

Cupertino, CA

$216K - $324K/yr

As part of the team you would work on core camera/ISP/Machine learning technologies, including ... Design and implement camera features in embedded systems for Apple products. You will work with ...

Apple

Camera Embedded Software Engineer

Cupertino, CA

$150K - $277K/yr

As part of the team you would work on core camera/ISP/Machine learning technologies, including ... Design and implement camera features in embedded systems for Apple products. You will work with ...

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Hourly Embedded Machine Learning information

What is an hourly embedded machine learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

How does an hourly embedded machine learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What are the key skills and qualifications needed to thrive as an hourly embedded machine learning engineer, and why are they important?

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What are the most commonly searched types of Embedded Machine Learning jobs in California?

The most popular types of Embedded Machine Learning jobs in California are:

What are popular job titles related to Hourly Embedded Machine Learning jobs in California?

For Hourly Embedded Machine Learning jobs in California, the most frequently searched job titles are:

What job categories do people searching Hourly Embedded Machine Learning jobs in California look for?

The top searched job categories for Hourly Embedded Machine Learning jobs in California are:

What cities in California are hiring for Hourly Embedded Machine Learning jobs?

Cities in California with the most Hourly Embedded Machine Learning job openings:

Hourly Embedded Machine Learning jobs near you
Infographic showing various Hourly Embedded Machine Learning job openings in California as of July 2026, with employment types broken down into 1% Internship, 89% Full Time, 7% Part Time, 1% Temporary, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Senior Machine Learning Engineer

Nvidia

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Re-posted 3 days ago

Key responsibilities

  • Design, train, and optimize machine learning models for LiDAR/camera perception and multi-sensor fusion.

  • Develop and coordinate ML workflows, including data pipelines, model training, metrics, and performance reporting.

  • Take ML models from evaluation to product deployment on the NVIDIA DRIVE AV platform, developing efficient product code in C++.

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies

Job description

Intelligent machines powered by Artificial Intelligence computers that can learn, reason, and interact with people are no longer science fiction. Today, a self-driving vehicle powered by AI can meander through a country road at night and find its way. NVIDIA's GPU runs Deep Learning algorithms, simulating human intelligence, and acts as the brain of computers, robots, and self-driving vehicles that can perceive and understand the world.

Our team is building the machine learning backbone of the Perception component for NVIDIA DRIVE AV. We are seeking the best Machine Learning Engineers with a background in computer vision, LiDAR & camera perception, multi-sensor fusion and AI infrastructure who are passionate about solving the hardest problems for self-driving cars. Are you interested in inventing human-level AI for perception under real world conditions? If so, join us!

What You'll Be Doing:

  • Model Development: Design, train, and optimize innovative machine learning models for LiDAR/camera perception and multi-sensor fusion (e.g., object detection/classification, image classification, semantic segmentation, tracking).

  • Develop and coordinate entire ML workflows, covering data pipelines, model training, model metrics, continuous performance instrumentation, and reporting.

  • Productization: Take ML models and algorithms from initial evaluation and experimentation all the way to product level on the NVIDIA DRIVE AV platform, developing highly efficient product code in C++.

  • Innovation: Keep track of the latest developments in machine learning, and incorporate techniques that improve platform performance.

  • Collaborate with LiDAR/camera teams, developers, engineers, and managers to turn complex ideas into reliable solutions for autonomous driving.

What We Need to See:

  • MS or PhD in Computer Science, Engineering, or a related field, or equivalent experience.

  • 8+ years of relevant proven industry experience applying machine learning to address real-world problems.

  • Strong C++ and Python programming and debugging skills with experience in developing for large, complex systems.

  • Deep practical experience applying machine learning to lidar/camera perception and multi-sensor fusion in automotive or related fields.

  • Experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and a strong understanding of the mathematical foundations of ML.

  • Building and sustaining training and essential metric workflows for large-scale datasets.

  • Excellent communication and analytical skills. Self-motivated drive to solve hard problems.

Ways to Stand Out From the Crowd:

  • Multi-sensor fusion, LiDAR or Camera Perception Experience: Proven track record of developing and shipping deep learning models for LiDAR/Camera and Multi-Sensor Fusion in a production environment.

  • Advanced Model Knowledge: Familiarity with modern network architectures like Transformers and their application to visual recognition tasks.

  • AV Production Experience: A history of delivering ML features and models into a production autonomous vehicle stack or a related robotics product.

  • Performance Optimization: Experience with model optimization for real-time inference on embedded or automotive platforms (e.g., using TensorRT).

We believe that realizing self-driving vehicles will be a defining contribution of our generation (e.g., traffic accidents are responsible for ~1.25 million deaths per year worldwide). We have the funding and scale, but we need your help on our team. NVIDIA is widely considered to be one of the technology world's most desirable employers with some of the most brilliant and talented people in the world working here. If you're creative and autonomous, we want to hear from you!

#AutonomousVehicles

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until September 4, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

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