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Machine Learning Engineer Quantization Jobs in Chester, NJ

1Kosmos

AI / Machine Learning Engineer

Iselin, NJ · Hybrid

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

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

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 ...

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

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 ...

Prudential

Lead, Machine Learning Engineer

Newark, NJ · On-site

$107K - $141K/yr

As a Lead, Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals to implement machine learning models that will deliver stability ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Chester, NJ

Machine Learning Engineer Quantization information

See Chester, NJ salary details

$33.4K

$136.4K

$204.9K

How much do machine learning engineer quantization jobs pay per year?

As of Jul 14, 2026, the average yearly pay for machine learning engineer quantization in Chester, NJ is $136,385.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $164,200.00 per year, depending on experience, location, and employer.

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 cities near Chester, NJ are hiring for Machine Learning Engineer Quantization jobs? Cities near Chester, NJ with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
AI / Machine Learning Engineer

AI / Machine Learning Engineer

1Kosmos

Iselin, NJ • Hybrid

Full-time

PTO

Re-posted 4 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

Apply