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Machine Learning Engineer Quantization Jobs in California

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site

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 ...

Qubeaxis

Machine Learning Engineer

San Francisco, CA · On-site

$130 - $180/hr

Job Title Machine Learning Engineer Job ID 20985 Location Work Mode Onsite About the Team Our ML Platform team builds intelligent systems that power recommendations, forecasting, ranking ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

Description Apple's Video Computer Vision (VCV) Face and Body technologies team is looking for a skilled Machine Learning Engineer with experience developing ML models for computer vision and ...

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Machine Learning Engineer Quantization information

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 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 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 August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

Pattern AI

San Mateo, CA • On-site

Full-time

Re-posted 17 days ago

Job description

Company Description

PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems.

Job Description

We're seeking an outstanding ML Engineer to join our data team and help build out best-in-class machine learning solutions on our platform, powering innovative solutions in marketing & sales and commercial analytics.

Responsibilities:

  • Build and deploy the ML pipelines that power PatternAI's machine learning platform.

  • Manage MLOps infrastructure to monitor and optimize models.

Qualifications

Experience:

  • 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.

  • Proficiency across topics in machine learning and statistics.

  • Fluency in Python coding as well as data manipulation (SQL, Spark, Pandas)

  • Broad familiarity with the Python ecosystem and common libraries including Scikit-Learn, XGBoost, PyTorch, Keras, Tensorflow, Pandas, and common ML cloud services.

  • Familiarity with CNNs, RNN, LSTMs, and the latest research trends.

  • Experience implementing, deploying, and maintaining production machine learning systems.

  • Experience monitoring and optimizing model performance.

  • Experience with Linux, Docker and AWS, and basic development operations.

  • Advanced degree in computer science, mathematics, statistics or related area of study strongly preferred.

Additional Information

About PatternAI

PatternAI is an early stage startup that is growing rapidly and recently closed a successful round of venture funding. We are emerging from stealth and with an exciting series of machine learning products and a rapidly growing number of enterprise customers.

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

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