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Deep Learning Quantization Jobs in Raleigh, NC (NOW HIRING)

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Deep Learning Quantization information

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$10.7K

$81.5K

$136.1K

How much do deep learning quantization jobs pay per year?

As of Jul 14, 2026, the average yearly pay for deep learning quantization in Raleigh, NC is $81,544.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,000.00 and $135,100.00 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 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 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 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.
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Deep Learning Quantization jobs near you
Sr Software Engineer, AI Tools - Quantizer

Sr Software Engineer, AI Tools - Quantizer

Qualcomm

Raleigh, NC • On-site

$119K - $157K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Qualcomm rating

9.6

Company rating: 9.6 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

5th of 209 rated software companies

Job description

Job Summary:
Qualcomm Technologies, Inc. is a leading technology innovator that drives digital transformation and creates a smarter, connected future. As a Senior Software Engineer in AI Tools, you will develop and implement machine learning techniques for quantization tooling, collaborating with cross-functional teams to optimize AI models on Qualcomm platforms.
Responsibilities:
• Collaborate with cross-functional teams in the AI Software team at Qualcomm to gain knowledge of the capabilities of QAIRT SDK and use it to optimize inference of AI models on Qualcomm AI accelerator IP.
• Validate and optimize the performance and accuracy of quantized models through detailed analysis and testing of machine learning use cases.
• Debug complex issues, perform root cause analysis, and ensure high system reliability.
• Contribute to the team's adoption of agentic AI workflows — leveraging tools such as Claude Code, or similar frameworks — to automate quantization experimentation, hyperparameter search, and model evaluation pipelines.
• Participate in design and code reviews.
• Work independently and lead junior team members. Your decision-making will impact your direct area of work and the work group.
Qualifications:
Required:
• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• PhD in Computer Science, Engineering, Information Systems, or related field.
• Bachelor's degree or equivalent in Engineering, Information Systems, Computer Science, or related field
• 4+ years software development experience using Python and/or C/C++
• Strong software development skills (e.g. data structure and algorithm design, object oriented or other software design paradigm knowledge, software debugging and testing, etc.)
• Strong communication skills (verbal, presentation, written)
• Solid understanding of Machine Learning and Deep Learning theory
• Experience working with one of the Deep Learning frameworks like PyTorch, Tensorflow, ONNX, JAX
Preferred:
• 2+ years Python programming experience
• Experience / exposure to Quantization techniques – PTQ, QAT, AWQ, SpinQuant, etc.
• Familiarity with different NN architectures: DNNs, CNNs, RNNs/LSTMs, GANs, LLMs, etc.
• Experience with AIMET, TorchAO or other quantization-focused libraries in the PyTorch ecosystem
• Familiarity with GenAI model architectures and challenges working with them – LLM, LVM, LMM
• Experience with optimizing software, specifically AI graph workloads, for embedded platforms
• Familiarity with agentic AI frameworks (e.g. Claude Code) and experience applying them to automate experimentation or evaluation workflows
• Ability to collaborate across a globally diverse team and multiple interests
Company:
Qualcomm designs wireless technologies and semiconductors that power connectivity, communication, and smart devices. Founded in 1985, the company is headquartered in San Diego, USA, with a team of 10001+ employees. The company is currently Late Stage.

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About Qualcomm

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Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including 5G-enabled smartphones that double as pro-level cameras and gaming devices, smarter vehicles and cities, and the technology behind the smart, connected factories that manufactured your latest purchase. Our powerful connectivity solutions keep you connected—even in remote areas. Qualcomm 5G and AI innovations are the power behind the connected intelligent edge. You’ll find our technologies behind and inside the innovations that deliver significant value across multiple industries and to billions of people every day.

Industry

Technology, communication and media

Company size

10,000+ Employees

Headquarters location

San Diego, CA, US

Year founded

1985

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