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Deep Learning Quantization Jobs (NOW HIRING)

E Ink

Deep Learning Researcher 1900S-2

Billerica, MA · On-site

$130K - $160K/yr

Develop and evaluate novel deep learning models for complex physical and chemical systems in ... Knowledge of model efficiency techniques (pruning, quantization, distillation). * Familiarity with ...

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

Develop deep learning models for prototyping and production purposes according to product feature ... Hands-on experience with model optimization (e.g., network quantization and mixed-precision ...

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

Develop deep learning models for prototyping and production purposes according to product feature ... Hands-on experience with model optimization (e.g., network quantization and mixed-precision ...

Cognex

Senior Machine Learning Engineer

Natick, MA

$115K - $225K/yr

Experience with embedded ML, quantization, or hardwareaware optimization. * Handson experience building and deploying efficient deep learning models for realworld computer vision applications.

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Deep Learning Quantization information

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$11K

$83.9K

$140K

How much do deep learning quantization jobs pay per year?

As of Jul 14, 2026, the average yearly pay for deep learning quantization in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 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.
More about Deep Learning Quantization jobs
What cities are hiring for Deep Learning Quantization jobs? Cities with the most Deep Learning Quantization job openings:
What states have the most Deep Learning Quantization jobs? States with the most job openings for Deep Learning Quantization jobs include:
What job categories do people searching Deep Learning Quantization jobs look for? The top searched job categories for Deep Learning Quantization jobs are:
Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 25% Part Time, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.
Sr. Computer Vision Engineer (Deep Learning)

Sr. Computer Vision Engineer (Deep Learning)

Harbinger

Mountain View, CA • On-site

$124K - $170K/yr

Full-time

Posted 21 days ago

Job description

Job Summary:
Harbinger is an American commercial electric vehicle (EV) company on a mission to transform an industry starving for innovation. We are seeking a highly skilled Senior Deep Learning Engineer to drive the development and deployment of advanced perception models for Advanced Driver Assistance Systems (ADAS).
Responsibilities:
• Design and implement advanced deep learning architectures to enhance perception capabilities within ADAS systems.
• Maintain and continuously improve existing models by optimizing performance, addressing issues, and refining architecture and algorithms.
• Perform detailed root cause analysis of production issues and develop sustainable, high-quality solutions.
• Optimize model performance with a focus on latency, efficiency, and resource utilization for real-time embedded deployment.
• Integrate and validate deep learning algorithms on automotive-grade hardware and embedded SoCs.
• Collaborate closely with data engineering, data annotation, and platform engineering teams to ensure smooth data flow and seamless model integration.
• Provide regular updates and technical reports on model development, maintenance progress, and performance metrics to management.
Qualifications:
Required:
• 5+ years of professional experience developing, training, validating, and deploying deep learning-based perception models for ADAS or related computer vision applications.
• In-depth understanding of training and inference pipelines, including data loading, augmentation, and loss function design.
• Advanced degree (M.S. or Ph.D.) in Computer Vision, Robotics, Machine Learning, or a closely related discipline, or equivalent industry experience.
• Strong proficiency in Python and a deep understanding of software design principles and development best practices.
• Expertise in PyTorch (preferred) or TensorFlow for large-scale model development and experimentation.
• Practical experience with data pipelines, distributed training, and machine learning experiment management tools.
• Proven ability to work effectively in a collaborative, cross-functional team environment.
Preferred:
• Comprehensive understanding of machine learning algorithms, including classification, regression, and clustering methods.
• Experience deploying and optimizing models for embedded or automotive SoCs (e.g., NVIDIA Drive, TI TDA4, Qualcomm Snapdragon).
• Proficiency in model optimization techniques such as quantization, pruning, and knowledge distillation.
• Doctorate (Ph.D.) in Computer Science, Artificial Intelligence, or related field is a plus.
• Strong programming experience in Python and/or C++ within Linux development environments.
• Familiarity with automotive perception workflows, datasets, and evaluation frameworks (e.g., KITTI, Waymo, Euro NCAP)
Company:
Harbinger is a commercial electric vehicle company that focuses on chassis architecture design. Founded in 2021, the company is headquartered in Garden Grove, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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