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Machine Learning Engineer Quantization Jobs in Pennsylvania

Motional

Machine Learning Engineer, Data Mining

Pittsburgh, PA · On-site +1

$111K - $133K/yr

As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain ... quantization) to ensure models run efficiently in production environments. * Data Mining & Analysis:

Merican

AI / Machine Learning Engineer

PA · On-site

AI / Machine Learning Engineer (Contract) Location: Philadelphia, PA or Charlotte, NC Duration: 6 Months Contract Job Summary We are seeking an experienced AI / Machine Learning Engineer to design ...

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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.

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What job categories do people searching Machine Learning Engineer Quantization jobs in Pennsylvania look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Pennsylvania are:
What cities in Pennsylvania are hiring for Machine Learning Engineer Quantization jobs? Cities in Pennsylvania with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in Pennsylvania as of July 2026, with employment types broken down into 85% Full Time, 9% Part Time, 1% Temporary, 4% Contract, and 1% Nights. Highlights an 85% Physical, 7% Hybrid, and 8% Remote job distribution.

Machine Learning Engineer

SR Talent Solution Inc.

Pittsburgh, PA • On-site

Other

Posted yesterday

Job description

Position: Machine Learning Engineer

Location: Uptown Pittsburgh, PA-Onsite

Duration: Full Time


What You'll Do

Build, train, and evaluate ML models for defect detection and classification on imagery captured from live inspection lines.

Develop ML models for anomaly detection and classification in cases where labeled defects are rare or hard to define.

Optimize and deploy models to run on edge hardware at the inspection line, balancing accuracy against latency and throughput constraints.

Partner with hardware and embedded systems engineers to integrate models into the end-to-end inspection pipeline, from camera capture to real-time decision.

Establish and improve the data workflow - labeling, dataset curation, augmentation, and retraining loops - to keep models sharp as products and conditions change.

Diagnose model performance issues in production and on-site, using real line data to drive improvements.

Help define ML standards, tooling, and best practices that the broader team will build on.

What We’re Looking For

Experience training, evaluating, and deploying deep learning models using Pytorch or Tensorflow, preferably for computer vision applications.

Hands-on experience with classical image processing, unsupervised/self-supervised learning methods, preferably applied to anomaly detection. Solid grounding in classical computer vision and statistical modeling.

Experience with or strong interest in vision-language models for tasks like zero/few-shot classification, and prompt driven anomaly detection.

Familiarity with model optimization for edge deployments – quantization, ONNX/TensorRT, and profiling models under latency and memory constraints.

A pragmatic, results-oriented mindset - comfortable working with messy real-world data and iterating quickly.

Willingness to work on-site and collaborate closely with hardware and embedded teammates.

Bonus: experience in manufacturing, industrial inspection, or other real-time/high-throughput vision systems.

Shelfmark: Automated, in-line web inspection. Made possible by managed AI.

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