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Deep Learning Quantization Jobs in Pittsburgh, PA

Waabi

Distillation Lead

Pittsburgh, PA · On-site

$195K - $286K/yr

... quantization). - Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas. - Experience distilling large generative ...

Waabi

Distillation Lead

Pittsburgh, PA

$195K - $286K/yr

... quantization). - Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas. - Experience distilling large generative ...

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All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Pittsburgh, PA

Deep Learning Quantization information

See Pittsburgh, PA salary details

$10.7K

$81.4K

$135.9K

How much do deep learning quantization jobs pay per year?

As of May 29, 2026, the average yearly pay for deep learning quantization in Pittsburgh, PA is $81,437.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,900.00 and $134,900.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Deep Learning Quantization Engineer, and why are they important?

To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.

What are some common challenges faced when implementing deep learning quantization in production environments?

One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.

What is deep learning quantization?

Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.

What is the difference between Deep Learning Quantization vs Machine Learning Engineer?

AspectDeep Learning QuantizationMachine Learning Engineer
Required CredentialsAdvanced degrees in AI, Computer Science, or related fields; knowledge of neural networksBachelor's or Master's in CS, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, hardware optimization settingsSoftware development teams, data-driven projects, product-focused environments
Industry UsageAI hardware optimization, model deployment, edge computingModel development, data analysis, software solutions across industries

Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.

What are popular job titles related to Deep Learning Quantization jobs in Pittsburgh, PA? For Deep Learning Quantization jobs in Pittsburgh, PA, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Pittsburgh, PA look for? The top searched job categories for Deep Learning Quantization jobs in Pittsburgh, PA are:
Deep Learning Quantization jobs near you
Staff/Principal Engineer - Perception Capabilities

Staff/Principal Engineer - Perception Capabilities

Motional

Pittsburgh, PA • On-site

Other

Posted 11 days ago

Job description

Mission Summary:

As a seasoned expert in Perception Capabilities, you'll field cutting-edge software products and solutions using state-of-the-art computer vision and deep learning. Your breadth of experience spans critical problem areas like detection, classification, segmentation, and tracking, along with essential supporting functions such as data engines, labeling policies, training pipelines, model optimization, simulation, and testing. You're driven by seeing your work significantly impact the product, taking initiative and owning outcomes.

You're eager to apply these skills to the perception stack of self-driving vehicles. You understand the unique challenges of safety-critical, open-world applications and are committed to ensuring engineering excellence and rigorous evaluation.

You'll devise creative solutions to complex problems, collaborating with interdisciplinary, capability-focused teams across the organization to design, implement, and validate end-to-end solutions.

What you'll be doing:

  • Lead the development and implementation of solutions that span across perception components and directly interface with downstream modules in the autonomy system.
  • Analyze the performance of the perception subsystem to proactively identify potential limitations with respect to a set of system-level capabilities. Document potential hazards and lead the cross-team efforts to eliminate or mitigate them.
  • Collaborate on innovative solutions to address "long tail" challenges. For example, developing techniques for synthetic data generation, advanced data mining, or leveraging language embeddings in our onboard models.
  • Depending on your background and interests, you may also help optimize and deploy models to the vehicle, ensuring they operate within the constraints of our onboard hardware and meet our functional requirements and interface specifications.
  • Interface with systems and safety engineers, test engineers, and data analysts to ensure appropriate requirements, test coverage, and performance metrics

What We're Looking for:

  • Master's or Ph.D. in Machine Learning, Computer Science, Robotics, or a related field; or equivalent industry experience
  • Strong leadership skills in executing large, complex technical initiatives
  • Track record of successful interdisciplinary work and product-focused engineering
  • Broad understanding of Deep Learning algorithms, architectures, and applications
  • Experience designing, training, and analyzing neural networks for applications relevant to perception
  • Fluency in Python, including common libraries for deep learning (PyTorch), scientific computing, and data analysis and visualization
  • Advanced knowledge of software engineering principles, including software design, source control management, build processes, code reviews, and  testing methods 
  • Excellent communication and interpersonal skills
  • Experience mentoring and leading others

Bonus Points:

  • Experience shipping a "real" product and balancing ongoing support with next-generation innovation
  • Experience developing safety-critical products or components
  • Publications in relevant conferences (CVPR, ICML, NeurIPS, etc.)
  • Strong programming skills in C++ and/or CUDA programming
  • Experience with TensorRT and model quantization

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