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Embedded Machine Learning Engineer Jobs in Trenton, NJ

Indeed

Machine Learning Engineer

Philadelphia, PA

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain and improve model inference services. You will learn and apply new techniques from open source ...

Toogeza

Senior Machine Learning Engineer

Philadelphia, PA

$105K - $144K/yr

Clearly communicate complex technical concepts to non-engineering stakeholders in an accessible, outcome-focused way. What we're looking for An MS or PhD in Computer Science, Machine Learning ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Trenton, NJ

Embedded Machine Learning Engineer information

See Trenton, NJ salary details

$70.2K

$153.8K

$174.5K

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

As of Jun 8, 2026, the average yearly pay for embedded machine learning engineer in Trenton, NJ is $153,806.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,900.00 and $173,500.00 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.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Trenton, NJ? For Embedded Machine Learning Engineer jobs in Trenton, NJ, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Trenton, NJ look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Trenton, NJ are:
What cities near Trenton, NJ are hiring for Embedded Machine Learning Engineer jobs? Cities near Trenton, NJ with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you
Infographic showing various Embedded Machine Learning Engineer job openings in Trenton, NJ as of May 2026, with employment types broken down into 82% Full Time, 16% Part Time, and 2% Temporary. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $153,806 per year, or $73.9 per hour.

Machine Learning Engineer

Guru Schools

Philadelphia, PA • Hybrid

Other

Posted 6 days ago

Job description

Overview:
Machine Learning Engineer
Philadelphia, PA OR Washington, DC | Hybrid: 3-4 days/week
9 + Months
Role:
Design and validate ML models that support engineering tooling teams.
Enhance existing AIML automation tools (e.g., Speech data), implement LLM prompt interactions, and use LLMs to test LLMs - with a strong focus on product quality.
Key Responsibilities:
Build & enhance ML/AI models for validation and automation
Implement prompt-based LLM interactions
Collaborate across tooling squads and cross-functional teams
Contribute to POC development in AI/ML & Computer Vision
Requirements:
4+ years overall experience
1+ year hands-on ML model experience
Strong quality-focused mindset with LLM expertise
NLP, data engineering & model deployment experience
Tech Used:
GPT, LLMs, NLP, internet-developed tools
Interview Process:
2 Rounds
Skills:
Design and validate ML models that support engineering tooling teams. Enhance existing AIML automation tools (e.g., Speech data), implement LLM prompt interactions, and use LLMs to test LLMs

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