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Embedded Machine Learning Engineer Jobs in Trenton, NJ

Varsity Tutors

Machine Learning Tutor

New Brunswick, NJ · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Trenton, NJ · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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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 Aug 25, 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 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 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 August 2026, with employment types broken down into 1% As Needed, 68% Full Time, 26% Part Time, 3% Temporary, and 2% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $153,806 per year, or $73.9 per hour.

Principal Machine Learning Engineer

Philadelphia, PA • On-site

Apetan Consulting llc
IT Services • 1 - 10 employees
Learn more about Apetan Consulting llcexternal

$80 - $150/hr

Contractor

Re-posted 8 days ago

Job description

Title: Principal Machine Learning Engineer

Duration: 6 Mos C2H (without sponsorship)

Location: Hybrid in Philadelphia, PA onsite Tue & Wed each week (Local candidates preferred but, those willing to relocate are acceptable)

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.

Hands-On Model Development

  • Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.
  • Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.
  • Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.
  • Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.
  • Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries.
  • Move quickly from data exploration to prototype to validated model to production-ready capability.

 

Required Qualifications

  • Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.
  • 5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.
  • 3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.
  • Strong hands-on experience with Python and SQL.
  • Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.
  • Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.
  • Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.
  • Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.
  • Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.
  • Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.

Scoring, Scorecards, and Transparent Models

Production ML and MLOps

Product and Rapid-Build Execution

Generative AI and AI Automation

Requirement Shaping and Stakeholder Partnership