ZipRecruiter

Log In

1

Machine Learning Engineer Quantization Jobs in Ewing, NJ

Slalom Consulting

General Information

Philadelphia, PA · On-site

$60.50 - $78.75/hr

Own the end-to-end machine learning production lifecycle, including data ingestion, feature engineering, model deployment, monitoring, and lifecycle management. * Develop, maintain, and optimize ...

Nestle

Senior Data Scientist

Bridgewater, NJ

Hands-on programming experience in Python and R, leveraging advanced cognitive and machine learning methods to solve business challenges. * Experience working with relational database and cloud ...

Redolent, Inc.

AI/ML Engineer

Pennington, NJ · On-site

Design, engineer, and curate training and validation datasets to support machine learning pipelines ... for intent classification, speech recognition, and conversational AI performance optimization

Pwc

AI Solutions Engineering Delivery Lead

Philadelphia, PA · On-site

$103K - $136K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Redolent, Inc.

AI/ML Software Engineer

Pennington, NJ · On-site

We are seeking an AI/ML Software Engineer for a contract position with a Global Financial ... Work closely with cross functional teams to build scalable applications, develop machine learning ...

previous page

Showing results 41-60

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

Machine Learning Engineer Quantization information

See Ewing, NJ salary details

$30.2K

$123.5K

$185.6K

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

As of Sep 2, 2026, the average yearly pay for machine learning engineer quantization in Ewing, NJ is $123,523.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $148,700.00 per year, depending on experience, location, and employer.

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 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 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 Ewing, NJ are hiring for Machine Learning Engineer Quantization jobs?

Cities near Ewing, NJ with the most Machine Learning Engineer Quantization job openings:

Machine Learning Engineer Quantization jobs near you

AI/ML Integration Specialist - ACWS

Data Systems Analysts, Inc.

Fort Dix, NJ • On-site

Full-time

Posted 14 days ago

Job description

AI/ML Integration Specialist Summary: Lead AI/ML integration by delivering practical, secure, and governed capabilities that improve productivity, automation, search, analytics, and decision support while ensuring proposed AI/ML solutions are authorized, usable in Army environments, aligned with mission and compliance requirements, and implemented without disrupting operations.

Required Skills: 

  • Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Information Systems, or a related technical field. 
  • AWS Certified Machine Learning Engineer, AWS Certified Generative AI Developer or AI/ML equivalent certification and Comp Tia Sec+
  • Active Secret Clearance
  • 6+ years of experience supporting AI/ML, data science, analytics, intelligent automation, software integration, or enterprise application modernization. 
  • Experience designing and integrating AI/ML capabilities into enterprise applications, workflows, APIs, data services, reporting tools, or user-facing systems. 
  • Working knowledge of machine learning, natural language processing, generative AI, prompt engineering, model evaluation, data pipelines, and responsible AI practices. 
  • Experience with Python, SQL, APIs, cloud services, structured and unstructured data, and integration patterns used to connect AI/ML capabilities with operational systems. 
  • Ability to translate mission needs, user pain points, and business requirements into practical AI/ML use cases, prototypes, implementation plans, and measurable outcomes. 
  • Knowledge of secure software delivery, data protection, privacy, model governance, human-in-the-loop controls, and compliance considerations for federal or regulated environments. 
  • Strong collaboration skills with the ability to work across architecture, development, data, cybersecurity, DevSecOps, testing, product, and government stakeholder teams. 
  • Active Secret Clearance

Desired Qualifications: 

  • Experience identifying and implementing AI/ML use cases for federal acquisition, contracting, financial, procurement, or mission workflow systems
  • Experience with AI-enabled acquisition capabilities such as document analysis, clause/compliance assistance, contextual help, search, summarization, anomaly detection, workload assignment, recommendation engines, reporting, and decision support.
  • Experience integrating AI/ML capabilities with Appian, LC/NC platforms, Java services, APIs, databases, business intelligence tools, or cloud-hosted environments. 
  • Experience developing AI-enabled search, document analysis, workflow automation, anomaly detection, recommendation, summarization, reporting, or decision-support capabilities. 

Responsibilities: 

  • Lead identification, design, and integration of AI/ML use cases that improve ACWS modernization, user efficiency, workflow automation, analytics, and decision support. 
  • Translate operational needs and stakeholder requirements into AI/ML solution concepts, technical requirements, prototypes, backlog items, and implementation guidance. 
  • Partner with architects, data administrators, developers, DevSecOps, cybersecurity, testers, and functional analysts to integrate AI/ML capabilities into secure enterprise delivery. 
  • Support AI/ML model selection, data preparation, prompt design, integration patterns, validation planning, and performance monitoring. 
  • AI-enabled delivery, testing, validation, and release support.
  • Implement responsible AI practices, including human review, explainability considerations, data protection, access control, auditability, and appropriate governance. 
  • Coordinate testing and validation of AI/ML-enabled capabilities to ensure accuracy, reliability, security, usability, and alignment with mission outcomes. 
  • Maintain AI/ML documentation, design assumptions, use case traceability, model/integration decisions, risks, and implementation recommendations. 
  • Evaluate emerging AI/ML technologies and recommend practical improvements that support ACWS modernization without disrupting operational continuity. 

Apply