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Machine Learning Engineer Quantization Jobs in Simi Valley, CA

Voxelcloud

Machine Learning Engineer

Los Angeles, CA

... engineering, or mathematics * 2-3 years of relevant experience in building deep learning solutions ... Hands-on experience with model optimization (e.g., network quantization and mixed-precision ...

Voxelcloud

Machine Learning Engineer

Los Angeles, CA · On-site

... engineering, or mathematics * 2-3 years of relevant experience in building deep learning solutions ... Hands-on experience with model optimization (e.g., network quantization and mixed-precision ...

Machina Labs

Machine Learning Engineer

Los Angeles, CA

$160K - $190K/yr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of what's possible in smart manufacturing. In this role, you will design, build, train, and deploy ...

Machina Labs

Machine Learning Engineer

Chatsworth, CA · On-site

$160K - $190K/yr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of what's possible in smart manufacturing. In this role, you will design, build, train, and deploy ...

Motorola

Machine Learning Engineer

Los Angeles, CA

$150K - $180K/yr

Bachelor degree with 4+ years experience as a machine learning engineer * AND 2+ years of Python and PyTorch or TensorFlow experience * Must be a U.S. citizen with the ability to obtain necessary ...

Match Group

Machine Learning Engineer II

Los Angeles, CA · On-site

$105K - $143K/yr

Machine Learning Engineers (this role) who focus on modeling and algorithmic innovation * Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training ...

Match Group

Machine Learning Engineer II

Los Angeles, CA · On-site +1

$145K - $165K/yr

Machine Learning Engineers (this role) who focus on modeling and algorithmic innovation * Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training ...

Match Group

Machine Learning Engineer II

Los Angeles, CA · On-site +1

$105K - $143K/yr

Machine Learning Engineers (this role) who focus on modeling and algorithmic innovation * Machine Learning Infrastructure Engineers who build the platforms and tools that enable scalable training ...

Capgroup

Machine Learning Engineer II

Los Angeles, CA

$105K - $143K/yr

In this role you will work with a high performing team of applied scientists, machine learning engineers, and software development engineers that has delivered a number of AI/ML systems to production ...

Bespoke Labs

Machine Learning Engineer

Northridge, CA · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Simi Valley, CA

Machine Learning Engineer Quantization information

See Simi Valley, CA salary details

$32.5K

$132.9K

$199.8K

How much do machine learning engineer quantization jobs pay per year?

As of Jul 30, 2026, the average yearly pay for machine learning engineer quantization in Simi Valley, CA is $132,928.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,800.00 and $160,000.00 per year, depending on experience, location, and employer.

What are some common challenges Machine Learning Engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer Quantization, and why are they important?

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

What does a Machine Learning Engineer Quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

What is the difference between Machine Learning Engineer Quantization vs Data Scientist?

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in Simi Valley, CA? For Machine Learning Engineer Quantization jobs in Simi Valley, CA, the most frequently searched job titles are:
What cities near Simi Valley, CA are hiring for Machine Learning Engineer Quantization jobs? Cities near Simi Valley, CA with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you

Machine Learning Engineer

Voxelcloud

Los Angeles, CA

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 3 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.

Apply