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Machine Learning Engineer Quantization Jobs in California

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 ...

Poesis LLC

Machine Learning Engineer

Menlo Park, CA

Poesis Machine Learning Engineer At Poesis, machine learning and artificial intelligence open the door to improved alpha discovery, higher quality decision-making and intelligent risk management. We ...

TetraMem Inc

Senior Machine Learning Engineer

San Jose, CA · On-site

$200K - $280K/yr

Improve inference efficiency and model compression techniques, including quantization, pruning, and ... Engineering, Machine Learning, or related fields. * Must have prior experience managing a team ...

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 ...

LeanData

Machine Learning Engineer

Santa Clara, CA

Machine Learning Engineer LeanData helps the world's fastest-growing companies automate, simplify, and accelerate revenue. We are looking for a curious and innovative Machine Learning Engineer to ...

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

$122K - $184K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: Artificial ... quantization, edge inference and related fields. Come join us on this exciting journey. In this ...

PULSE

Machine Learning Engineer

San Francisco, CA

Optimize inference, batching, and quantization on GPU * Productionize models with clear SLAs and ... Solid engineering discipline and metrics focus Nice to have * Triton Inference Server, TensorRT ...

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

$122K - $184K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: Artificial ... quantization, edge inference and related fields. Come join us on this exciting journey. In this ...

Skild AI

Machine Learning Engineer

San Mateo, CA

$100K - $300K/yr

Machine Learning Engineer San Mateo, Pittsburgh Company Overview At Skild AI, we are building the world's first general purpose robotic intelligence that is robust and adapts to unseen scenarios ...

Eragon

Machine Learning Engineer

San Francisco, CA

Machine Learning Engineer We're looking for a Machine Learning Engineer to build and deploy production-grade AI systems. In this role, you'll take models from research to real-world applications ...

Winaxis

Machine Learning Engineer

Fremont, CA · On-site

Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only h1 candidate About the Role: Our direct client is hiring a Machine Learning Engineer for their ...

Qualcomm

Modem Machine Learning Engineer

San Diego, CA · On-site

Qualcomm Technologies, Inc. is seeking a Modem Machine Learning Engineer who will apply advanced ... quantization, and neural network optimization tools. • Familiarity with cloud ML platforms (e.g ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

MM International

Machine Learning Engineer

Fremont, CA · On-site

Machine Learning Engineer Location: Fremont, CA once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a ...

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Machine Learning Engineer Quantization information

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 California? For Machine Learning Engineer Quantization jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in California look for? The top searched job categories for Machine Learning Engineer Quantization jobs in California are:
What cities in California are hiring for Machine Learning Engineer Quantization jobs? Cities in California with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
Infographic showing various Machine Learning Engineer Quantization job openings in California as of June 2026, with employment types broken down into 100% Part Time. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution.
Machine Learning Engineer

Machine Learning Engineer

Voxelcloud

Los Angeles, CA • On-site

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 24 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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