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Deep Learning Quantization Jobs in New Jersey (NOW HIRING)

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · Hybrid

Strong understanding of deep learning architectures for image and text recognition. * Familiarity ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

Strong understanding of deep learning architectures for image and text recognition. * Familiarity ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · On-site

Strong understanding of deep learning architectures for image and text recognition. * Familiarity ... Preferred Qualifications * Experience with model quantization and optimization for mobile ...

Pwc

AI Engineer

Florham Park, NJ · On-site

$50K - $112K/yr

... networks and deep learning methods for advanced AI applications - Managing data quality and ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in New Jersey

Deep Learning Quantization information

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

To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.

What is the difference between Deep Learning Quantization vs Machine Learning Engineer?

AspectDeep Learning QuantizationMachine Learning Engineer
Required CredentialsAdvanced degrees in AI, Computer Science, or related fields; knowledge of neural networksBachelor's or Master's in CS, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, hardware optimization settingsSoftware development teams, data-driven projects, product-focused environments
Industry UsageAI hardware optimization, model deployment, edge computingModel development, data analysis, software solutions across industries

Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.

What is deep learning quantization?

Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.

What are some common challenges faced when implementing deep learning quantization in production environments?

One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.
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Deep Learning Quantization jobs near you

AI / Machine Learning Engineer

Apogee Global RMS

Piscataway, NJ • On-site

Full-time

Posted 6 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