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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 ...

Advatix Inc.

Machine Learning Engineer

San Mateo, CA · On-site

$110K - $165K/yr

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift: N/A Location: San Mateo, CA (Peninsula) - Onsite Preferred Schedule: Full time, Permanent Role Visa ...

Tapestry

Machine Learning Engineer

Mountain View, CA · On-site

About the role: We're looking for an early career Machine Learning Engineer to join our team. In this role you will build and deploy state of the art machine learning models to solve complex ...

Tapestry

Machine Learning Engineer

Mountain View, CA · On-site

About the role: We're looking for an early career Machine Learning Engineer to join our team. In this role you will build and deploy state of the art machine learning models to solve complex ...

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 ...

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 ...

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 ...

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 ...

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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 September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 84% Physical, 2% Hybrid, and 14% Remote job distribution.

Machine Learning Engineer

Pleasanton, CA • On-site

Indotronix International Corporation
Recruiting and Staffing Services • 1 - 5K employees
Learn more about Indotronix International Corporationexternal

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This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Machine Learning Engineer (Azure Focus) - Remote (PST)
[About the Role]
Join GAP as a Machine Learning Engineer and lead the development, deployment, and optimization of innovative AI solutions in a fully remote environment. This six-month contract opportunity is tailored for engineers with a passion for hands-on machine learning in Microsoft Azure production settings. Collaborate with talented teams to create real business impact by delivering robust, scalable models for enterprise applications.
[Responsibilities]
- Design, build, and deploy machine learning models using neural networks and NLP techniques to solve business challenges
- Manage the complete ML lifecycle: data preparation, model selection, training, evaluation, deployment, and ongoing performance monitoring
- Leverage Python and ML frameworks (TensorFlow, PyTorch, Keras) to develop, optimize, and maintain models
- Implement MLOps best practices, including CI/CD pipelines, model versioning, and automated retraining in Azure environments
- Collaborate cross-functionally with data scientists, engineers, and stakeholders to deliver production-ready AI solutions
- Utilize Azure Machine Learning, Azure DevOps, and Azure Databricks for end-to-end ML operations
[Required Skills and Experience]
- Proven track record developing and deploying machine learning models into Microsoft Azure cloud platforms
- Hands-on expertise with neural networks and NLP methods (text classification, sentiment analysis, entity extraction, chatbots, or language models)
- Advanced proficiency in Python; practical experience with R and SQL for data extraction and statistical modeling
- Deep understanding of supervised and unsupervised learning algorithms, model evaluation, and optimization
- Mastery of modern ML frameworks (TensorFlow, PyTorch, Keras) in production
- Demonstrable experience in MLOps: CI/CD for ML, model monitoring, version control, retraining, and production support
[Preferred Skills]
- Experience with Azure Databricks, Azure Functions, and Azure Pipelines
- Familiarity with containerization (Docker/Kubernetes) for scalable ML deployments
- Strong feature engineering and ML pipeline automation skills
[Benefits]
- 100% remote work-collaborate with a diverse team from anywhere within PST hours
- Opportunity for contract extension and career progression on impactful enterprise AI projects
- Exposure to best-in-class Azure ML infrastructure and enterprise DevOps practices
[How to Apply]
Excited to build and deploy cutting-edge AI solutions in Azure? Submit your resume now. Qualified candidates will be contacted for an initial screening and technical interview.
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About Indotronix

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In 1986, Indotronix established itself in the staffing space. 22 years later, Avani entered the scene, offering consulting and technology development. Finally, in 2016, the two joined forces to begin delivering talent across all areas, from Staffing to Consulting to unique platform development.

Industry

Recruiting and staffing services

Company size

1,001 - 5,000 Employees

Headquarters location

Rochester, NY, US

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