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Embedded Machine Learning Engineer Jobs in Los Angeles, CA

Stay current with the latest machine learning research for wireless and embedded systems, applying ... Experience with Linux, DevOps (command line) * Experience with containerized infrastructure (Docker ...

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling advanced software systems to automate Design for Manufacturing analysis. Responsibilities : • ...

As a Senior Machine Learning Engineer, you will design, build, and scale advanced software systems that automate Design for Manufacturing analysis, leveraging deep learning and computer vision ...

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of what's possible in smart manufacturing. In this role, you will design, build, train, and deploy ...

Machine Learning Engineer

Chatsworth, CA · On-site

$160K - $190K/yr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of what's possible in smart manufacturing. In this role, you will design, build, train, and deploy ...

Machine Learning Engineer

Torrance, CA · On-site

$160K - $250K/yr

As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling these systems end to end. What You'll Do: * Research, develop and deploy cutting-edge deep learning ...

Stay current with the latest machine learning research for wireless and embedded systems, applying ... Experience with Linux, DevOps (command line) * Experience with containerized infrastructure (Docker ...

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Showing results 1-20

Embedded Machine Learning Engineer information

See Los Angeles, CA salary details

$75.4K

$165.3K

$187.5K

How much do embedded machine learning engineer jobs pay per year?

As of Jul 18, 2026, the average yearly pay for embedded machine learning engineer in Los Angeles, CA is $165,272.00, according to ZipRecruiter salary data. Most workers in this role earn between $141,700.00 and $186,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Embedded Machine Learning Engineer, and why are they important?

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What job categories do people searching Embedded Machine Learning Engineer jobs in Los Angeles, CA look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Los Angeles, CA are:
What cities near Los Angeles, CA are hiring for Embedded Machine Learning Engineer jobs? Cities near Los Angeles, CA with the most Embedded Machine Learning Engineer job openings:
Machine Learning Engineer

Machine Learning Engineer

Motorola Solutions

Los Angeles, CA • On-site

Full-time

Posted 2 days ago


Motorola Solutions rating

8.7

Company rating: 8.7 out of 10

Based on 40 frontline employees who took The Breakroom Quiz

11th of 143 rated electronics manufacturers


Job description

Job Summary:
Motorola Solutions is a global community focused on keeping people safer. They are seeking a Machine Learning Engineer to develop solutions that enhance performance in wireless communication systems, impacting products and services that utilize signal processing technology.
Responsibilities:
• Manage inputs gathered from unusual sources, including captures from software defined radio (SDR) over a wide range of RF signals
• Combine knowledge of signal processing, probability and statistics, machine learning, and modern methods of artificial intelligence to build large-scale and high-throughput systems handling vast quantities of data
• Collaborate with UX designers, infrastructure engineers, and other research scientists to develop prototypes and integrate ML algorithms that work across a wide range of scales from resource-constrained edge compute to full-sized data centers
• Stay current with the latest machine learning research for wireless and embedded systems, applying ingenuity and a deep understanding of the problems at hand
Qualifications:
Required:
• 4+ years experience as a machine learning engineer
• Expert knowledge in Python and an ML framework such as PyTorch or TensorFlow
• Strong foundation in supervised and unsupervised learning and statistical modeling
• Strong mathematics background, particularly in linear algebra and probability
• Strong written and oral communication skills
• Bachelor degree with 4+ years experience as a machine learning engineer
• AND 2+ years of Python and PyTorch or TensorFlow experience
• Must be a U.S. citizen with the ability to obtain necessary security clearance as required by government contract.
Preferred:
• Advanced degree in a quantitative field such as electrical and computer engineering, physics, mathematics or statistics
• Familiarity with relational and NoSQL databases
• Familiarity with RF signal processing and SDR for signals intelligence or electronic warfare
• Familiarity with cloud-based infrastructure: Azure and/or AWS
• Experience tracking projects with Jira, Azure DevOps or similar tooling
• Experience with Linux, DevOps (command line)
• Experience with containerized infrastructure (Docker, Kubernetes)
• Familiarity with regulated environments, such as sovereign clouds
Company:
Motorola Solutions creates mission-critical communication solutions and services for public safety and commercial customers. Founded in 1928, the company is headquartered in Chicago, USA, with a team of 10001+ employees. The company is currently Late Stage.

What Motorola Solutions employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Motorola Solutions logo

About Motorola Solutions

Sourced by ZipRecruiter

At Motorola Solutions, we believe that everything starts with safety. It's the constant that empowers people to confidently move forward. It can fill a flight or sell out a stadium. It can care for a patient or graduate a class. As a global leader in public safety and enterprise security, we create and connect the technologies that help to keep people safe where they live, learn, work and play. Our integrated technology ecosystem unifies critical communications, video security and access control, and command center software, enabling collaboration in more powerful ways. At Motorola Solutions, we're ushering in a new era in public safety and security. Bring your passion, potential and talents to a career that matters.

Industry

Technology, communication and media

Company size

10,000+ Employees

Headquarters location

Chicago, IL, US

Year founded

1928