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Machine Learning Engineer Quantization Jobs in Los Angeles, CA

We're looking for a Machine Learning Engineer to train custom models using our internal data. This work spans design generation, cost estimation, evaluating the complexity and difficulty of a given ...

Sr Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

We are seeking a hands-on Senior Machine Learning Engineer to support and enhance machine learning platforms used for media measurement and customer analytics. This role partners closely with Data ...

The Role Our client is seeking a Machine Learning Engineer to help design and implement intelligent systems that extract meaning and predictive value from computer vision and behavioral datasets.

New

We're looking for a Machine Learning Engineer to train custom models using our internal data. This work spans design generation, cost estimation, evaluating the complexity and difficulty of a given ...

We're looking for a Machine Learning Engineer to train custom models using our internal data. This work spans design generation, cost estimation, evaluating the complexity and difficulty of a given ...

As a Machine Learning Integration Engineer, you will help rapidly prototype, mature, and monitor ML/CV solution that are integral to Turion's Space Domain Awareness data products. You will work on ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Showing results 41-60

Machine Learning Engineer Quantization information

See Los Angeles, CA salary details

$33.9K

$138.8K

$208.5K

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

As of Aug 9, 2026, the average yearly pay for machine learning engineer quantization in Los Angeles, CA is $138,750.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,400.00 and $167,000.00 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 job categories do people searching Machine Learning Engineer Quantization jobs in Los Angeles, CA look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Los Angeles, CA are:
What cities near Los Angeles, CA are hiring for Machine Learning Engineer Quantization jobs? Cities near Los Angeles, CA with the most Machine Learning Engineer Quantization job openings:

Machine Learning Engineer, Level 5

Snap Inc.

Los Angeles, CA • On-site

Full-time

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


Job description

Job Summary:
Snap Inc is a technology company that operates Snapchat and other digital services. They are looking for a Machine Learning Engineer to build and deploy machine learning models, solve large-scale problems, and partner with teams to launch ML-driven features.
Responsibilities:
• Build and deploy machine learning models that power core products, serving millions of Snapchatters
• Apply modern ML techniques to solve large-scale, real-world problems
• Own the full ML lifecycle from data analysis to production deployment
• Partner with cross-functional teams to prototype and launch ML-driven features
• Utilize AI tools to design and ship scalable services while upholding rigorous standards for code correctness, security, and production
Qualifications:
Required:
• Strong understanding of machine learning approaches and algorithms
• Able to prioritize duties and work well on your own
• Ability to work with both internal and external partners
• Skilled at solving open ambiguous problems
• Strong collaboration and mentorship skills
• Proficiency in, or a strong aptitude for, leveraging AI tools to streamline development, paired with the critical judgment to audit generated output for architectural integrity, performance bottlenecks, and security risks
• Adaptability in learning and applying evolving AI systems and tools to remain at the forefront of engineering trends and modern development practices
• Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
• 5+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 1 years of post-grad machine learning experience
• Experience developing machine learning models for ranking, recommendations, search, content understanding, image generation, or other relevant applications of machine learning
Preferred:
• Advanced degree in computer science or related field
• Experience working with machine learning frameworks such as TensorFlow, Caffe2, PyTorch, Spark ML, scikit-learn, or related frameworks
• Experience working with machine learning, ranking infrastructures, and system design
Company:
Snap is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Founded in 2011, the company is headquartered in Venice, USA, with a team of 5001-10000 employees. The company is currently Late Stage.