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Deep Learning Quantization Jobs in New Jersey (NOW HIRING)

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 · 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 ...

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

AI / Machine Learning Engineer

1Kosmos

Iselin, NJ • On-site

Full-time

PTO

Re-posted 7 days ago

Job description

Are you ready to shape the future of authentication? Join 1Kosmos and help lead the next wave in identity assurance and passwordless innovation.
1Kosmos is driving the future of identity security, empowering organizations to eliminate passwords and establish trust at every step of the identity lifecycle. As a vibrant team of innovators, we develop advanced authentication solutions trusted by some of the world's leading brands. Join us as we create a passwordless world and set new standards for digital identity assurance.
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 fraud detection, delivering high-performance models that can run on both mobile devices and cloud environments.
This role blends R&D with production engineering-you'll take ownership of the full ML lifecycle, from dataset creation and model training to deployment and performance optimization.
Key Responsibilities
  • Design and implement AI models for image classification, object detection, OCR, and feature extraction.
  • Develop real-time image quality assessment and capture guidance algorithms.
  • Create and maintain data pipelines for collecting, cleaning, augmenting, and labeling datasets.
  • Implement model optimization techniques for mobile (on-device) and cloud inference.
  • Apply fraud detection methods to identify tampering, forgeries, or replay attacks in visual data.
  • Integrate ML models into production-grade APIs and mobile SDKs.
  • Monitor, evaluate, and continuously improve model accuracy and performance.
  • Collaborate with product and engineering teams to align AI capabilities with business goals.

Requirements
  • Bachelor's or Master's degree in Computer Science, AI/ML, or related field (or equivalent experience).
  • 3+ years of experience building and deploying ML models in production.
  • Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or similar).
  • Experience with computer vision libraries (e.g., OpenCV) and OCR technologies.
  • Strong understanding of deep learning architectures for image and text recognition.
  • Familiarity with cloud platforms (AWS, GCP, or Azure) and API development.
  • Strong problem-solving skills and ability to work in fast-paced environments.
  • Based in the NJ / NY area; Hybrid working model.
Preferred Qualifications
  • Experience with model quantization and optimization for mobile deployment.
  • Knowledge of synthetic data generation and data augmentation techniques.
  • Background in security, liveness detection, or anomaly detection.
  • Exposure to compliance and data privacy regulations (GDPR, CCPA).
  • Contributions to open-source ML projects or published research.

Benefits
  • Cutting-Edge Tech Stack: Build with decentralized identity protocols, FedRamp High, FIDO2-certified cryptography, and NIST-compliant biometric systems.
  • Accelerated Growth: Receive annual stipends for certifications and attend key conferences like Identiverse or EIC.
  • Ownership & Impact: We move fast and will enable you to make a big impact with large customers in US & Canada.
  • Flexibility First: Unlimited PTO, and 2 days WFH

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