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Hourly Embedded Machine Learning Jobs in California

Liquid XR

Machine Learning Engineer

Los Angeles, CA · On-site +1

The Role We are seeking a Machine Learning Engineer to develop advanced models for extracting ... Exposure to embedded or edge deployment constraints * Background in applied domains involving ...

Motorola

Machine Learning Engineer

Los Angeles, CA · On-site

$150K - $180K/yr

Stay current with the latest machine learning research for wireless and embedded systems, applying ingenuity and a deep understanding of the problems at hand Required Skills * 4+ years experience as ...

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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.

Embedded Systems Engineer at Insight Global Mountain View, CA

Insight Eye Care

Mountain View, CA • On-site

$60 - $80/hr

Other

Posted 6 days ago

Job description

Embedded Systems Engineer job at Insight Global. Mountain View, CA.

This role can pay between $45-$50/hour depending on years of experience and skillset.

Responsibilities
  • The architect and development of embedded systems for intelligent edge sensing applications
  • Developing firmware and drivers for embedded systems
  • Collaborating with ML engineers to implement real-time ML sensing application
  • Optimizing embedded ML model performance in terms of power, latency and memory usage
  • Create prototypes to demonstrate new edge sensing features
Skills and Requirements
  • Bachelor's degree in Computer Science, Electrical Engineering, or equivalent experience
  • 3+ years of experience working in an embedded system technical environment
  • Experience in developing machine learning-based image and voice embedded system applications using C or C+
  • Experience with MCUs, RTOS, memories, interfaces, and sensors
  • Proficient in low-power AI processor, sensor solutions, and overall system architecture
  • Proficient in firmware and driver developments for embedded systems
  • Proficient in embedded machine learning model development

We are a company committed to creating inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity employer that believes everyone matters.

Qualified candidates will receive consideration for employment opportunities without regard to race, religion, sex, age, marital status, national origin, sexual orientation, citizenship status, disability, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to Human Resources Request Form ( .

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