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Embedded Machine Learning Engineer Jobs in Sacramento, CA

Staffingine LLC

AI Engineer

Woodland, CA · On-site

AI Engineer Job Location: Woodland - California Job Type: Contract ... Design develop and deploy machine learning models and algorithms using Python Lead data science ...

Pwc

US Tech - AI Engineering Manager

Sacramento, CA

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Crowe LLP

AI Infrastructure Engineer

Sacramento, CA · On-site

$114K - $150K/yr

Engineer advanced ADO pipeline patterns (multi-stage, gated, reusable templates) * Solve complex ... You've gone deeper than reading blog posts, ideally having several example machine learning ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Sacramento, CA

Embedded Machine Learning Engineer information

See Sacramento, CA salary details

$74.6K

$163.6K

$185.5K

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

As of Jul 19, 2026, the average yearly pay for embedded machine learning engineer in Sacramento, CA is $163,556.00, according to ZipRecruiter salary data. Most workers in this role earn between $140,200.00 and $184,500.00 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.

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What cities near Sacramento, CA are hiring for Embedded Machine Learning Engineer jobs? Cities near Sacramento, CA with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you
AI Engineer

AI Engineer

Staffingine LLC

Woodland, CA • On-site

Contractor

Re-posted 26 days ago

Job description

Job Title: AI Engineer
Job Location: Woodland - California
Job Type: Contract

Job Description:

  • Design develop and deploy machine learning models and algorithms using Python Lead data science projects from concept to implementation ensuring timely delivery and quality outcomes
  • Perform exploratory data analysis to identify patterns trends and opportunities for business improvement
  • Collaborate with stakeholders to define key performance indicators and success metrics Optimize existing data science workflows and models for better performance and accuracy
  • Document methodologies code and findings to ensure reproducibility and knowledge sharing
  • Support the integration of data science solutions into production environments
  • Drive continuous improvement initiatives by evaluating new tools and technologies relevant to Python and data science

Skills

Mandatory Skills : Python - Data Scienc