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Machine Learning Engineer Quantization Jobs in Pittsburgh, PA

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 ...

Gather AI

Director of Machine Learning

Pittsburgh, PA · On-site

... Cloud Engineering and Director of Autonomy to ensure ML systems integrate seamlessly into the ... Machine Learning, or a related field (strong industry track record considered in lieu of advanced ...

Varsity Tutors

Machine Learning Tutor

Pittsburgh, PA · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Pwc

AI Engineer

Pittsburgh, PA · On-site

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

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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

Director of Machine Learning

Gather AI

Pittsburgh, PA • On-site

Full-time

Re-posted 13 days ago

Job description

About Us

Are you ready to build the future of supply chain? At Gather AI, we're not just creating software; we're pioneering a new era of warehouse intelligence. We've developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining "on-time, in full" delivery.

If you're looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We're leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time.

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 and Business leadership to translate ML capabilities into product value. This is a high-visibility role with a direct line to executive leadership and significant influence over product direction.

About the Role

We are looking for a Director of Machine Learning to define our ML strategy, lead our computer vision organization, and drive the next phase of ML maturity at Gather AI. You will assess existing ML and CV capabilities, identify gaps and opportunities, and establish a clear technical vision for how machine learning will power our warehouse intelligence platform over the next 12–18 months.

The ideal candidate brings deep computer vision expertise, a track record of shipping production ML systems, and the leadership experience to build and scale a world-class ML team.

What You'll Do

  • Define and own the ML strategy and technical roadmap for Gather AI, aligned with product and business objectives
  • Lead and grow the Machine Learning and FPT teams, establishing a culture of rigor, experimentation, and production-quality delivery
  • Drive improvements to core computer vision models (object detection, segmentation, OCR) used across our drone and MHE Vision products
  • Build out MLOps infrastructure — model training pipelines, deployment, monitoring, and CI/CD for ML workloads
  • Collaborate with the Director of Cloud Engineering and Director of Autonomy to ensure ML systems integrate seamlessly into the broader platform
  • Partner with Product and Operations to translate customer needs into ML-driven product capabilities

What You'll Need

  • 10+ years building and scaling production ML or computer vision systems
  • 5+ years managing and growing ML engineering teams
  • Deep expertise in computer vision: object detection, image segmentation, OCR, and CNN architectures
  • Strong Python and PyTorch (or TensorFlow) proficiency, plus a track record of shipping ML models to production at scale
  • MS or PhD in Computer Science, Machine Learning, or a related field (strong industry track record considered in lieu of advanced degree)

Nice to Have

  • Experience with drone, robotics, or autonomous systems perception
  • Edge ML deployment experience (ONNX, TensorRT, mobile/embedded inference)
  • Familiarity with warehouse, logistics, or supply chain domain
  • Experience with AWS or GCP ML services (SageMaker, Vertex AI)