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Deep Learning Quantization Jobs in Los Angeles, CA

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 ...

Mach Industries

Perception Engineer

Huntington Beach, CA · On-site

$125K - $220K/yr

You'll work across deep learning, computer vision, and embedded systems to bring research-grade ... Optimize models for real-time embedded inference (quantization/pruning, TensorRT/ONNX Runtime ...

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Los Angeles, CA

Deep Learning Quantization information

See Los Angeles, CA salary details

$11.9K

$90.4K

$150.9K

How much do deep learning quantization jobs pay per year?

As of Aug 10, 2026, the average yearly pay for deep learning quantization in Los Angeles, CA is $90,388.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,600.00 and $149,800.00 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

Machine Learning Engineer

Voxelcloud

Los Angeles, CA

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 14 days ago

Job description

Company Description

Founded in 2016, VoxelCloud, Inc. is a Los Angeles-based worldwide leader in AI analysis of medical images.  Backed by Sequoia and Tencent.  We help healthcare providers make better/earlier diagnoses and related clinical decisions, improving outcomes for all.  http://www.voxelcloud.ai

Job Description

The R&D team (located in Los Angeles, CA) is involved with research and development of innovative solutions to medical imaging applications,  including disease detection/quantification in medical scans, disease risk stratification, image synthesis, text report mining,  and more! We are currently hiring both full-time and interns to join our R&D team.

Responsibilities:

  • Develop deep learning models for prototyping and production purposes according to product feature request
  • Design, implement and test model experiments using major deep learning frameworks
  • Document experiments findings and results with supporting summary statistics for peer discussion and review (Confluence)
  • Provide insights to data collection and annotation and collaborate with the data team for in-house data management and labelling
  • Write production and deployment code (dockerization), iterate deployed models for optimal performance and inference speed
  • Conduct methodology research in deep learning to drive scalable, real-time implementation
Qualifications

Basic Qualifications

  • MS degree in computer science, engineering, or mathematics
  • 2-3 years of relevant experience in building deep learning solutions for computer vision problems
  • Proficient with at least one major deep learning framework, preferably TensorFlow/Pytorch
  • Proficient in Python
  • Good CS fundamentals in data structures and algorithm
  • Detail-oriented, well organized and self-motivated with a continuous drive to learn, explore and be challenged
  • Work well in teams and communicate ideas clearly

Preferred Qualifications

  • PhD degree in computer science, engineering, or mathematics
  • 3-5 years of relevant experience in building deep learning solutions for computer vision problems
  • Hands-on experience with state-of-the-art object detection (e.g., RetinaNet, Mask RCNN, CenterNet), semantic segmentation (e.g., U-Net, deeplab), and image classification models (e.g., ResNet, DenseNet).
  • Track record of publications in CV and medical image analysis is a plus
  • Hands-on experience with model optimization (e.g., network quantization and mixed-precision training) is a plus
  • Prior experience with medial images is a plus
Additional Information

We Offer...  

  • An outstanding start-up culture; 
  • Transparent, collaborative work environment;
  • Competitive compensation
  • Excellent Medical, Dental, and Vision coverage
  • 401k, paid Vacation and Holiday

All your information will be kept confidential according to EEO guidelines.

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