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Deep Learning Quantization Jobs (NOW HIRING)

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 ...

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine ... quantization, deployment optimization). * Experienced in inference time optimization, deep ...

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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 Aug 28, 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 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.

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.

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Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Machine Learning Scientist

Laguna Hills, CA • On-site

Xforia, Inc.
IT Services • 51 - 200 employees
Learn more about Xforia, Inc.external

Contractor

Re-posted 10 days ago

Job description

ESSENTIAL JOB DUTIES AND RESPONSIBILITIES:
Rapid prototyping, training, and testing of ML solutions using online code repositories, research publications, or customer specifications.
Staying current with advancements in ML, including new development tools, libraries, frameworks, ML models/architectures, training techniques, and application pipelines.
Participating in ML algorithm/hardware co-design tasks.
Performing, documenting, and presenting detailed analyses related to ML algorithm development, software/hardware benchmarking, and application development.
Gaining a thorough understanding of the Akida 1.0/2.0 hardware device and associated software stack (MetaTF).
Interfacing with customers to discuss ML application goals, constraints, and opportunities.
Developing and optimizing models for time series data and large language models (LLMs).
Advancing the state-of-the-art in ML through innovative research and practical coding skills.
QUALIFICATIONS:
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Education/Experience:
Master's Degree in Computer Science, Electrical Engineering, or a related field with 5+ years of experience; or a PhD with 3+ years of experience.
Coursework in machine learning, computer vision, control systems, and time series modeling.
Strong programming skills in Python.
Experience developing ML applications in TensorFlow/Keras and/or PyTorch.
Excellent communication skills.
Experience in one or more of the following application fields: Image Processing/Computer Vision, ADAS, Anomaly Detection, Audio/Speech Processing, Automatic Speech Recognition, and Time Series Modeling
Evidence of creativity, including patents and publications.
Preferred Qualifications:
Experience training and optimizing large language models (LLMs).
Ph.D. 5+ years of domain expertise
Multi-project experience in object classification, object detection, face recognition, keyword spotting, and time series modeling, automatic speech recognition.
Knowledge of deep learning quantization techniques.
Experience with Docker and Git.
Experience with Scrum/Agile software development methodologies (e.g., Jira).
Language Skills:
Exceptional presentation, verbal and written skills.
Ability to independently synthesize a point of view given many different perspectives.
Ability to read and interpret documents, such as policies and procedures, routine mail, contracts, and instruction manuals. Ability to compose routine reports and correspondence.
Ability to effectively communicate with persons of various social, cultural, economic, and educational backgrounds.
Reasoning Ability:
Advanced ability to analyze information, problems, situations, practices, or procedures.
Advanced ability to analyze complex technical data using qualitative and quantitative sources of information to formulate logical and objective conclusions and to recognize alternatives and their implications.
Ability to carry out instructions delivered in written, oral, or other formats in daily situations.
Ability to deal with problems involving several concrete variables in standardized situations.
Ability to make timely decisions to produce positive outcomes.

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