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Embedded Machine Learning Engineer Jobs in New Jersey

Apex Systems

Machine Learning Engineer

Jersey City, NJ · On-site

Job#: 3049321 Machine Learning Engineer Location: Jersey City, New Jersey (Onsite) Employment Type: Contract Contract Duration: 12 Months Role Overview This position involves applying advanced ...

New

AvePoint

Senior Machine Learning Engineer

Jersey City, NJ · On-site

$190K - $220K/yr

We are seeking an analytical and innovative Senior Machine Learning Engineer to join our Data & AI team. You will play a key role in developing and deploying advanced machine learning models to solve ...

asrcfh

Senior Machine Learning Engineer

Moorestown, NJ

$103K - $141K/yr

We are currently seeking a Senior Machine Learning Engineer to join our team in Moorestown, NJ. Responsibilities: * Develops, researches, and applies machine learning, deep learning, visual ...

New

Xident B.V.

Senior Machine Learning Engineer

Union, NJ · On-site

$140 - $190/hr

Senior Machine Learning Engineer We're looking for a Senior ML Engineer to advance our age bracket classifiers and face recognition models. We run 5 binary classifiers (+12/+15/+18/+21/+25) deployed ...

New

Bespoke Labs

Machine Learning Engineer

Trenton, NJ · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in New Jersey

Embedded Machine Learning Engineer information

See New Jersey salary details

$71.1K

$155.7K

$176.7K

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

As of Sep 5, 2026, the average yearly pay for embedded machine learning engineer in New Jersey is $155,720.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $175,600.00 per year, depending on experience, location, and employer.

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 the key skills and qualifications needed to thrive as an embedded machine learning engineer?

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 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 New Jersey?

For Embedded Machine Learning Engineer jobs in New Jersey, the most frequently searched job titles are:

What job categories do people searching Embedded Machine Learning Engineer jobs in New Jersey look for?

The top searched job categories for Embedded Machine Learning Engineer jobs in New Jersey are:

What cities in New Jersey are hiring for Embedded Machine Learning Engineer jobs?

Cities in New Jersey with the most Embedded Machine Learning Engineer job openings:

Embedded Machine Learning Engineer jobs near you

Machine Learning Engineer

5 Star Global Recruitment Partners

Newark, NJ

Full-time

Posted 4 days ago

Job description

Machine Learning Engineer

Newark, New Jersey, United States

Job Description

As a Machine Learning Engineer, you will play a pivotal role in driving the development and implementation of cutting-edge machine learning solutions for our client. Your responsibilities will encompass a wide range of tasks, from leading a small team of machine learning engineers to collaborating with cross-functional teams to deliver impactful solutions. You will be at the forefront of driving innovation and leveraging the power of machine learning to solve real-world problems, drive business growth, and create value.

Key Responsibilities

  • Lead and drive machine learning projects from inception to production: build relationships with business partners and cross-functional teams.
  • Collaborate with business leaders, subject matter experts, and decision-makers to develop success criteria and optimize new products, features, policies, and models.
  • Partner with data scientists to understand, implement, train, and design machine learning models.
  • Collaborate with the infrastructure team to improve the architecture, scalability, stability, and performance of ML platform.
  • Construct optimized data pipelines to feed machine learning models.
  • Extend existing machine learning libraries and frameworks.
  • Develop processes, model monitoring, and governance framework for successful ML model operationalization.
  • Define objectives for the Machine Learning platform, own the technical roadmap, and be accountable for delivering results.
  • Define standards for engineering and operational excellence for running best-in-class ML platforms and continue to improve ML platforms to keep up with the latest innovations.
  • Design and implement the best architectural practices in the delivery of data science use cases.

Key Skills/Knowledge/Experience

  • 7+ years of experience in Machine Learning.
  • Extensive software engineering experience with strong working experience as a Machine Learning Engineer.
  • Bachelor's degree in computer science, computer engineering, or a related engineering field. Masters degree preferred.
  • Advanced proficiency with Python, Java, and Scala.
  • Strong computer science fundamentals such as algorithms, data structures, multithreading.
  • Experience working with Generative AI, using LangChain for Gen AI and techniques like RAG.
  • Experience using ML and DL Libraries:XGBoost, SKlearn, Tensorflow or PyTorch
  • In-depth experience building solutions using public clouds such as AWS, GCP.
  • Experience using ML platforms like SageMaker, H2O, DataRobot, etc.
  • Strong knowledge on ML model development life cycle components like containers, batch vs real time inference endpoints, application security testing etc.
  • Experience managing relationships in a cross-functional environment with multiple stakeholders.
  • Experience with developing and deploying production-grade applications with ML inferences using automation pipeline on cloud.
  • Experience working in Agile/ Scrum development process.
  • Thought leadership and innovative thinking.
  • Excellent communication and collaboration skills.

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