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Machine Learning Engineer Quantization Jobs in Lincoln, CA

Staffingine LLC

AI Engineer

Woodland, CA · On-site

AI Engineer Job Location: Woodland - California Job Type: Contract ... Design develop and deploy machine learning models and algorithms using Python Lead data science ...

Deloitte

Oracle AI Manager

Sacramento, CA

... machine learning operations, continuous integration/continuous delivery pipelines, and DevOps practices Experience applying AI solutions in finance, healthcare, or supply chain environments ...

Pwc

AI & GenAI Data Scientist - Manager

Sacramento, CA · On-site

$99K - $232K/yr

In this role at PwC, you will apply data, algorithms, and software engineering to build and deploy software and platform systems that create Artificial Intelligence and Machine Learning-based ...

Deloitte

Data Scientist

Sacramento, CA · On-site

... or Machine Learning role. * 5+ Years of Experience Proficiency in programming languages such as Python or R. * 5+ Years of Experience with Strong knowledge of machine learning techniques and ...

DataAnnotation

SDLC Engineer - AI Trainer

Roseville, CA · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

DataAnnotation

SDLC Engineer - AI Trainer

Sacramento, CA · Remote

$50 - $100/hr

As a DataAnnotation's coder, you'll be part of a growing community of over 100,000 professionals -- including front-end, back-end, full-stack, machine learning, and other engineers -- who are driving ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Lincoln, CA

Machine Learning Engineer Quantization information

See Lincoln, CA salary details

$32.9K

$134.4K

$202K

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 Lincoln, CA is $134,444.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,000.00 and $161,800.00 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 cities near Lincoln, CA are hiring for Machine Learning Engineer Quantization jobs? Cities near Lincoln, CA with the most Machine Learning Engineer Quantization job openings:
Machine Learning Engineer Quantization jobs near you
AI Engineer

AI Engineer

Staffingine LLC

Woodland, CA • On-site

Contractor

Posted 25 days ago

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Job description

Job Title: AI Engineer
Job Location: Woodland - California
Job Type: Contract

Job Description:

  • Design develop and deploy machine learning models and algorithms using Python Lead data science projects from concept to implementation ensuring timely delivery and quality outcomes
  • Perform exploratory data analysis to identify patterns trends and opportunities for business improvement
  • Collaborate with stakeholders to define key performance indicators and success metrics Optimize existing data science workflows and models for better performance and accuracy
  • Document methodologies code and findings to ensure reproducibility and knowledge sharing
  • Support the integration of data science solutions into production environments
  • Drive continuous improvement initiatives by evaluating new tools and technologies relevant to Python and data science

Skills

Mandatory Skills : Python - Data Scienc