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Machine Learning Engineer Quantization Jobs in Anaheim, CA

They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking ...

Senior Machine Learning Engineer

Irvine, CA ยท On-site

$111K - $153K/yr

Access on-demand professional development resources that allow you to hone existing skills and learn new ones "I can succeed as a Senior Machine Learning Engineer at Capital Group." You will join our ...

Fieldai Robotics Engineer FieldAI's Irvine team is where embodied AI meets real robots, real ... What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ...

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and creation of next-generation health monitoring devices. It is a cutting-edge research and ...

Engineer II, AI/Machine Learning

Irvine, CA ยท On-site

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and creation of next-generation health monitoring devices. It is a cutting-edge research and ...

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and creation of next-generation health monitoring devices. It is a cutting-edge research and ...

Senior Machine Learning Engineer

Los Angeles, CA ยท On-site

$112K - $154K/yr

... Machine Learning Engineering team to build the next generation of AI products at Capital Group - including agentic systems, LLM-powered workflows, and the platform that ensures they are safe ...

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Machine Learning Engineer Quantization information

See Anaheim, CA salary details

$33K

$134.8K

$202.6K

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

As of Jun 18, 2026, the average yearly pay for machine learning engineer quantization in Anaheim, CA is $134,809.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,300.00 and $162,300.00 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 Anaheim, CA? For Machine Learning Engineer Quantization jobs in Anaheim, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Quantization jobs in Anaheim, CA look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Anaheim, CA are:
What cities near Anaheim, CA are hiring for Machine Learning Engineer Quantization jobs? Cities near Anaheim, CA with the most Machine Learning Engineer Quantization job openings:
Infographic showing various Machine Learning Engineer Quantization job openings in Anaheim, CA as of June 2026, with employment types broken down into 86% Full Time, and 14% Part Time. Highlights an 87% Physical, 6% Hybrid, and 7% Remote job distribution, with an average salary of $134,809 per year, or $64.8 per hour.

2.53 3D Machine Learning Engineer

FieldAI

Irvine, CA โ€ข On-site

Full-time

Posted 7 days ago


Job description

Job Summary:
FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine learning models for processing reality capture data, contributing to automated progress tracking and scene understanding in construction environments.
Responsibilities:
โ€ข Design and implement scalable machine learning pipelines for large-scale 3D spatial data processing for point cloud analysis, object detection, segmentation, and scene understanding.
โ€ข Train, optimize, and deploy deep learning models using PyTorch, TensorFlow, or equivalent frameworks on cloud platforms such as AWS (e.g., SageMaker, EC2).
โ€ข Collaborate with software and systems engineers to integrate models into production environments and continuously improve inference pipelines.
โ€ข Analyze diverse sensor inputs, including RGBD imagery, LiDAR point clouds, 360 photos, audio, and Building Information Models (BIM).
โ€ข Work closely with the labeling and data operations teams to define robust data annotation strategies and ensure high model performance and generalization.
Qualifications:
Required:
โ€ข Bachelorโ€™s or Masterโ€™s degree in Computer Science, Machine Learning, Robotics, or a related technical field.
โ€ข 2+ years of hands-on industry experience developing and deploying machine learning systems for 3D point clouds, perception, or spatial understanding tasks.
โ€ข Strong background in 3D machine learning, with experience in deep learning for point clouds, multi-view fusion, or geometric learning.
โ€ข Strong expertise in Python and deep learning frameworks: PyTorch, TensorFlow, or similar.
โ€ข Familiarity with OpenCV and PCL (Point Cloud Library) for classical computer vision and 3D data preprocessing.
โ€ข Experience training, evaluating, and deploying ML models using cloud infrastructure (e.g., AWS, SageMaker) and containerized workflows.
โ€ข Solid understanding of the end-to-end ML lifecycle, including experiment tracking, reproducibility, model versioning, and optimization for production.
โ€ข Proven ability to work in fast-paced, interdisciplinary teams across software, ML, and product teams.
Preferred:
โ€ข Experience working with BIM data, digital twins, or construction-related sensor data.
โ€ข Background in geometric deep learning, 3D mesh analysis, GIS systems, or structured scene representations.
โ€ข Familiar with MLOps pipelines using Ray, SageMaker, MLflow, or Kubeflow.
โ€ข Strong foundation in geometric computer vision, robotics, or algorithmic 3D reasoning.
โ€ข Exposure to graph neural networks, geodesic computations, or neural implicit representations (e.g., NeRF, Occupancy Networks).
โ€ข Deep experience with point cloud and graph learning frameworks such as Open3D-ML, Torch-Points3D, PyG, or MMDetection3D.
โ€ข Experience building custom modules for SparseConvNet or 3D transformers.
Company:
FieldAI is building general robot intelligence for the physical world. Founded in 2023, the company is headquartered in Mission Viejo, USA, with a team of 201-500 employees. The company is currently Growth Stage.