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Machine Learning Engineer Quantization Jobs in Pittsburgh, PA

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Machine Learning Systems Engineer

Pittsburgh, PA · On-site +1

$144K - $192K/yr

We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier ...

Gather AI

Director of Machine Learning

Pittsburgh, PA · On-site

About the Team You'll lead the Machine Learning and FPT teams, working closely with the Director of Cloud Engineering and Director of Autonomy. Cross-departmentally, you'll collaborate with Product ...

Gather AI

Director of Machine Learning

Pittsburgh, PA · On-site

About the Team You'll lead the Machine Learning and FPT teams, working closely with the Director of Cloud Engineering and Director of Autonomy. Cross-departmentally, you'll collaborate with Product ...

Gather AI

Director of Machine Learning

Pittsburgh, PA · On-site +1

About the Team You'll lead the Machine Learning and FPT teams, working closely with the Director of Cloud Engineering and Director of Autonomy. Cross-departmentally, you'll collaborate with Product ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Pittsburgh, PA

Machine Learning Engineer Quantization information

See Pittsburgh, PA salary details

$30.6K

$125K

$187.9K

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

As of Aug 14, 2026, the average yearly pay for machine learning engineer quantization in Pittsburgh, PA is $125,011.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $150,500.00 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 are popular job titles related to Machine Learning Engineer Quantization jobs in Pittsburgh, PA?

For Machine Learning Engineer Quantization jobs in Pittsburgh, PA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Quantization jobs in Pittsburgh, PA look for?

The top searched job categories for Machine Learning Engineer Quantization jobs in Pittsburgh, PA are:

What cities near Pittsburgh, PA are hiring for Machine Learning Engineer Quantization jobs?

Cities near Pittsburgh, PA with the most Machine Learning Engineer Quantization job openings:

Machine Learning Engineer Quantization jobs near you

Principal Machine Learning Scientist

Gather AI

Pittsburgh, PA • On-site

Full-time

Re-posted 13 days ago

Job description

Job Summary:
Gather AI is pioneering a new era of warehouse intelligence with its vision-powered platform that utilizes autonomous drones for real-time data capture. They are seeking a Principal Machine Learning Scientist to enhance their computer vision systems for warehouse inventory scanning, focusing on the full ML lifecycle to achieve significant business outcomes.
Responsibilities:
• Advance core computer vision model performance (object detection, segmentation, OCR) for warehouse inventory scanning across drone and MHE Vision platforms
• Own the full ML lifecycle from research and experiment design through production deployment and monitoring — applying rigorous ablation studies and SOTA methodology
• Collaborate with the ML infrastructure team on model optimization and deployment across cloud and edge inference targets (ONNX, TensorRT, quantization)
• Work with Operations and Product to understand customer needs and translate them into ML improvements with measurable business impact
• Provide technical leadership and mentorship to the ML team, raising standards for experiment design, model evaluation, and production readiness
• Explore next-generation perception capabilities, including embedded and on-prem inference optimization for new deployment targets
Qualifications:
Required:
• 10+ years of experience in machine learning or computer vision
• Deep expertise in CNNs, object detection, image segmentation, and OCR using PyTorch (preferred) or TensorFlow
• Strong Python proficiency and software engineering fundamentals; hands-on experience with OpenCV and GPU computing
• Track record of delivering production ML systems at scale, including model training, evaluation, and deployment
• MS or PhD in Computer Science, Machine Learning, Robotics, or a related field
Preferred:
• Experience with drone, robotics, or autonomous systems perception
• Publications in top vision or robotics conferences (CVPR, ICCV, ICRA, NeurIPS, CoRL)
• Experience designing and deploying models for real-time inference on constrained compute platforms
• Warehouse, logistics, or supply chain domain experience
Company:
We deliver the foundational intelligence layer for the intralogistics industry. Founded in 2017, the company is headquartered in Pittsburgh, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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