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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 ...

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

$122.80K - $184.20K/yr

Engineering Group, Engineering Group > Machine Learning Engineering General Summary: As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation ...

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 ...

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 · 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 ...

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 ...

Adobe, Inc.

Machine Learning Engineer

San Jose, CA · On-site

Machine Learning Engineer The Opportunity Join Adobe and be at the forefront of driving digital transformation. As a Machine Learning Engineer, you will play a key role in developing machine learning ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

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 graphics ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

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 graphics ...

Adobe

Machine Learning Engineer

San Jose, CA · On-site

Adobe is at the forefront of driving digital transformation and is seeking a Machine Learning Engineer to develop machine learning models and algorithms. The role involves collaborating with multi ...

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Machine Learning Engineer Quantization information

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 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 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 May 2026, with employment types broken down into 1% Internship, 57% Full Time, 39% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 87% Physical, 8% Hybrid, and 5% Remote job distribution.

Machine Learning Engineer

Happy Elements

San Francisco, CA

Full-time

Posted 20 days ago

Job description

Machine Learning Engineer
Full-time
Responsibilities
  • Build, maintain, and improve efficient and reliable data mining and machine learning models.
  • Design, implement and tune machine learning models, and provide performance feedback.
  • Work closely with data engineers to adapt and improve data pipelines for production models.
  • Work closely with software engineers in putting models into production (interface, SLA, scalability).Qualifications
  • Strong academic background required. MS in Computer Science or Machine Learning with 2+ years of industry experience or PhD in related field with 1+ years of industry experience required.
  • Expert in Python, and computation graph toolkits (e.g., Scikit-learn, Tensorflow). Solid experience with Python packages such as Numpy, Panda, and Scikit-learn.
  • Expert/Master in common families of machine learning models, feature engineering, feature selection techniques, and tuning of machine learning models.
  • Master with SQL or other relational database.
  • Master in building and productionizing end-to-end machine learning systems.
  • Knowledge and experience in cloud computing is a plus.
  • Extensive data modeling and data architecture skills.
  • Advanced math skills (linear algebra, Bayesian statistics, group theory).
  • Ability to consistently exercise independent discretion and judgment on significant matters.
  • Strong analytical, problem-solving and communication skills.
  • Ability to work in a team environment

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