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Hourly Embedded Machine Learning Jobs in New Jersey

Judge Group, Inc.

Data Scientist - III

Bridgewater, NJ · On-site

$65 - $75/hr

... Hourly Description: Job Title : Data Scientist Location : Basking Ridge, NJ (Hybrid ) About the Role We are seeking an experienced Data Scientist to build, deploy, and scale machine learning ...

Newell Brands

$137K - $147K/yr

These applications will consume curated data and machine learning outputs and expose intelligence through APIs, user-facing tools, embedded workflows, and automated decision systems. Design, develop ...

Newell Brands

Data Engineering Manager

Hoboken, NJ · On-site

$137K - $147K/yr

These applications will consume curated data and machine learning outputs and expose intelligence through APIs, user-facing tools, embedded workflows, and automated decision systems. Design, develop ...

DTCC

At DTCC, interns contribute to meaningful work while learning how the financial markets operate ... Competitive hourly compensation. * Hybrid work model: 3 days in office and 2 days remote, based on ...

Hatch Global Search

Software Engineer-C, Python

Matawan, NJ · Hybrid

$52 - $71.50/hr

... embedded systems development with C; parallel, distributed or complex system programing project experience; machine learning; writing software that manipulates data at the bit and byte level.

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Hourly Embedded Machine Learning information

What is an hourly embedded machine learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

How does an hourly embedded machine learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What are the key skills and qualifications needed to thrive as an hourly embedded machine learning engineer, and why are they important?

To thrive as an Hourly Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

What is the difference between Hourly Embedded Machine Learning vs Hourly Data Scientist?

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What are the most commonly searched types of Embedded Machine Learning jobs in New Jersey?

The most popular types of Embedded Machine Learning jobs in New Jersey are:

What are popular job titles related to Hourly Embedded Machine Learning jobs in New Jersey?

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

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

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

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

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

Hourly Embedded Machine Learning jobs near you
Infographic showing various Hourly Embedded Machine Learning job openings in New Jersey as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 2% Temporary, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.

Machine Learning Systems Engineer - Video Computer Vision

Mount Laurel Township, NJ • On-site

Socket.dev
Network Security • 1 - 10 employees
Learn more about Socket.devexternal

Other

Posted 8 days ago

Job description

The incredible potential of multimodal foundation models and large language models has unlocked machine learning applications that were previously thought infeasible. The Video Computer Vision (VCV) group is looking for a highly motivated and skilled Machine Learning Systems Engineer to help us ship cutting‑edge computer vision technology on Apple devices. The VCV organization has pioneered groundbreaking features like FaceID/FaceKit, Gaze/Hand Gesture Control, Body Tracking, and 2D/3D Scene Understanding fundamentally changing how millions of users interact with technology. We seamlessly balance research and product requirements to deliver pioneering, Apple‑quality experiences. By innovating across the full stack and partnering closely with hardware, software, and AI teams, we shape future products and bring our architectural vision to life.

Description

As a member of the Video Computer Vision team, you will train, evaluate, and deploy purpose‑built vision models on Apple hardware. You will develop innovative techniques to optimize model performance, efficiency, and scalability, ensuring a seamless user experience under strict on‑device constraints.

Minimum Qualifications

Bachelor's degree in Computer Science, Machine Learning, or a related discipline, and 3+ years of relevant industry experience. Strong ML fundamentals. Proven track record of writing high‑quality production code for shipped on‑device CV/ML features deployed on embedded platforms. Solid understanding of operating system fundamentals and extensive programming experience in Python and C++. Hands‑on experience with PyTorch and familiarity with the end‑to‑end ML lifecycle (data preprocessing, training, evaluation, and edge deployment). Experience with Supervised Fine‑Tuning (SFT) pipelines to adapt vision and multimodal foundation models for specialized, on‑device downstream tasks. Robust foundational understanding of machine learning architectures, specifically Multimodal LLMs and the integration of ML components into complex production systems.

Preferred Qualifications

Programming experience with Swift and familiarity with CoreML, CoreFoundation, and RealityKit frameworks. Fundamental knowledge of real‑time video pipelines, image transformations, and rendering loops. Experience optimizing models for neural network accelerators (e.g., Apple Neural Engine or mobile GPUs).

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