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Machine Learning Engineer Quantization Jobs in New Jersey

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · Hybrid

  • PTO

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

  • PTO

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

  • PTO

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

Hyperproof

AI / Machine Learning Engineer

Woodbridge, NJ · On-site

$100 - $130/hr

  • PTO

We are looking for an AI / Machine Learning Engineer to design, build, and deploy advanced computer ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

SageSure

Senior Machine Learning Engineer

Jersey City, NJ · On-site

$127K - $168K/yr

As a Senior Machine Learning Engineer, you'll play a crucial role in optimizing orchestration processes and ensuring fast and efficient model deployment and delivery. You'll work closely with ...

SageSure

Senior Machine Learning Engineer

Jersey City, NJ · On-site

$127K - $168K/yr

As a Senior Machine Learning Engineer, you'll play a crucial role in optimizing orchestration processes and ensuring fast and efficient model deployment and delivery. You'll work closely with ...

Bespoke Labs

Machine Learning Engineer

Edison, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Jersey City, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Paterson, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Newark, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Trenton, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Elizabeth, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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Machine Learning Engineer Quantization information

What are some common challenges machine learning engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

What are the key skills and qualifications needed to thrive as a machine learning engineer quantization, and why are they important?

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a machine learning engineer quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

What is the difference between Machine Learning Engineer Quantization vs Data Scientist?

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in New Jersey?

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

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

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

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

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

Machine Learning Engineer Quantization jobs near you

AI / Machine Learning Engineer

Apogee Global RMS

Piscataway, NJ

Full-time

Posted 16 days ago

Job description

Apogee Global RMS is seeking an AI / Machine Learning Engineer to support our enterprise client in New Jersey. This role will focus on designing, building, and deploying advanced AI and computer vision solutions that drive innovation in identity, security, automation, and digital trust.

The ideal candidate will have strong experience developing production-grade machine learning models, working across the full ML lifecycle-from data preparation and model development to deployment and optimization across cloud and mobile environments.

Key Responsibilities:
  • Design and develop AI/ML models for computer vision, image classification, object detection, OCR, and feature extraction.
  • Build real-time image quality assessment, data processing, and intelligent capture solutions.
  • Develop and maintain data pipelines for data collection, labeling, cleaning, and augmentation.
  • Optimize ML models for cloud and mobile/on-device inference.
  • Implement fraud detection, anomaly detection, and security-focused AI capabilities.
  • Integrate ML models into production APIs and software platforms.
  • Monitor model performance and continuously improve accuracy and scalability.
  • Collaborate with engineering, product, and business teams to deliver AI-driven solutions.

Requirements

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or related field (or equivalent experience).
  • 3+ years of experience building and deploying machine learning models in production environments.
  • Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or similar.
  • Experience with computer vision libraries (OpenCV) and OCR technologies.
  • Strong understanding of deep learning architectures for image and text recognition.
  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Strong problem-solving skills and ability to thrive in a fast-paced environment.
Preferred Qualifications:
  • Experience with model optimization and quantization for mobile deployment.
  • Knowledge of synthetic data generation and data augmentation techniques.
  • Background in fraud detection, anomaly detection, security, or identity technologies.
  • Familiarity with data privacy and compliance standards.
  • Experience contributing to open-source projects or AI research.
What You'll Do:
  • Build next-generation AI capabilities with real-world enterprise impact.
  • Work on innovative computer vision and machine learning challenges.
  • Partner with talented engineering teams to move AI solutions from research to production.
  • Drive improvements in accuracy, performance, and scalability.

Benefits

For any questions (OR) to apply, please contact us at careers@apogeeglobalrms.com.

Apply