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Embedded Machine Learning Engineer Jobs in New Jersey

Bespoke Labs

Machine Learning Engineer

Newark, NJ

* 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 ...

Bespoke Labs

Machine Learning Engineer

Paterson, 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 ...

Bespoke Labs

Machine Learning Engineer

Paterson, NJ

* 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 ...

Prudential

Lead, Machine Learning Engineer

Newark, NJ · On-site

As a Director, Machine Learning Engineer on/in >, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to build >. You will implement machine learning models ...

IPolarity

NJ · On-site

$140 - $190/hr

Machine Learning Engineer - GCP / Vertex AI / Dataproc / Apache Iceberg Location: Charlotte, NC. No OPT/CPT Key Responsibilities * Deploy and manage ML models using Google Vertex AI. * Build ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

Crowe LLP

Machine Learning Engineer

Livingston, NJ · On-site

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · Hybrid

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture, data extraction, and ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · Hybrid

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture, data extraction, and ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · Hybrid

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer vision and AI solutions. You will work on projects involving image capture , data extraction , and ...

Workiva

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

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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?

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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

Bespoke Labs

Newark, NJ

Full-time

Re-posted 19 days ago

Job description

  • 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, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems

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