ZipRecruiter

Log In

1

Deep Learning Quantization Jobs (NOW HIRING)

Targeted Talent

Senior Deep Learning Engineer

Austin, TX · On-site +1

$130K - $180K/yr

We're hiring 3 Senior Deep Learning Engineers to join our Neural Networks team. Your primary focus ... Familiarity with model compression techniques like quantization, pruning, etc. These are permanent ...

Hayden AI

Deep Learning Engineer II

San Francisco, CA · On-site

$161.64K - $175K/yr

Deep Learning Engineer II POSITION DUTIES: Lead the research, development, and deployment of ... Drive innovation in model compression, quantization, and efficient inference techniques to optimize ...

New

E Ink

Deep Learning Researcher

Billerica, MA · Hybrid

$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 ...

E Ink

Deep Learning Researcher 1900S-2

Billerica, MA · Hybrid

$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 ...

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 ...

next page

Showing results 1-20

People also search for

All JobsDeep Learning Quantization Jobs

Deep Learning Quantization information

See salary details

$11K

$83.9K

$140K

How much do deep learning quantization jobs pay per year?

As of Jun 3, 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 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.

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 May 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.
Senior Deep Learning Engineer

Senior Deep Learning Engineer

Targeted Talent

Austin, TX • On-site, Remote

$130K - $180K/yr

Full-time

Posted 22 days ago

Job description

We're seeking top-notch engineers to join our team. As part of our group, you'll collaborate with hardware and software engineers to design, develop, and optimize software for our chip, making AI inference accessible to everyone. You'll excel in identifying and resolving functional/performance bottlenecks in complex software and hardware designs.

We're hiring 3 Senior Deep Learning Engineers to join our Neural Networks team. Your primary focus will be optimizing neural networks to efficiently run on our hardware and building a model optimization pipeline. If you thrive on pushing the boundaries of AI technology, this role is for you!

Requirements:

  • Bachelor's degree in Computer Science, Engineering, or related field
  • 5+ years of experience, with at least 2 years in both deep learning and software engineering
  • Proficiency in deep learning frameworks like Tensorflow and/or PyTorch
  • Experience with CNNs, LSTMs/RNNs, Transformers
  • Strong math skills and Python proficiency
  • Experience with C/C++

Preferred Skills & Experience:

  • Master's or PhD in Computer Science, Engineering, or related field
  • Experience in embedded or low-level programming
  • Knowledge of CUDA/OpenGL
  • Experience deploying neural networks in production
  • Familiarity with model compression techniques like quantization, pruning, etc.
These are permanent full time remote positions.
Targeted Talent logo

About Targeted Talent

Sourced by ZipRecruiter

Your single source for HR professional services, we offer job seekers specialized employment services, spanning contract, permanent positions, and project solutions for highly specialized and managerial level talent needs. Our team of specialized recruiters and consultants abilities extend far beyond resume or career counseling. With hundreds of collaborators strategically located throughout the country, our organization possess the local market knowledge and industry relationships that make successful geography-specific reach possible.

Industry

Recruiting and staffing services

Company size

11 - 50 Employees

Headquarters location

Vancouver, BC, CA

View All Targeted Talent Jobs