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Machine Learning Engineer Quantization Jobs in Visalia, CA

Symbotic

System Engineer

Porterville, CA · On-site

The System Engineer focuses on troubleshooting,maintaining, and stabilizing complex electro ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

Symbotic Inc.

System Engineer

Porterville, CA · On-site

The System Engineer focuses on troubleshooting, maintaining, and stabilizing complex electro ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

Symbotic

System Engineer

Porterville, CA · On-site

The System Engineer focuses on troubleshooting, maintaining, and stabilizing complex electro ... Applying next-gen technology, high-density storage and machine learning to solve today's complex ...

Trilyon, Inc.

Program/Project Analyst 1

Tulare, CA · On-site

$30 - $32/hr

... Machine Learning, and Technical Writing, we consistently exceed expectations in catering to a wide ... Demonstrates working knowledge of 1 programming language, report production, and database ...

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

Machine Learning Engineer Quantization information

See Visalia, CA salary details

$32K

$130.9K

$196.7K

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

As of Sep 11, 2026, the average yearly pay for machine learning engineer quantization in Visalia, CA is $130,916.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,200.00 and $157,600.00 per year, depending on experience, location, and employer.

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 cities near Visalia, CA are hiring for Machine Learning Engineer Quantization jobs?

Cities near Visalia, CA with the most Machine Learning Engineer Quantization job openings:

Machine Learning Engineer Quantization jobs near you

Algorithm Development (Quant Research & Trading) PhD Internship - Summer 2027

London, CA • On-site

Hudson River Trading
See more jobs at Hudson River Tradingexternal

Full-time, Temporary, Internship

Re-posted 29 days ago

Job description

Hudson River Trading (HRT) is seeking exceptional full-time PhD students to join our Algorithm Development summer internship program. Algorithm Developers at HRT focus on the research and implementation of automated trading strategies.

We trade on more than 200 markets around the world, across a variety of time horizons - offering ample opportunities to explore innovative, self-guided research and make a big impact on our business. Through this internship, you'll have the opportunity to rotate across teams, learning and collaborating alongside researchers and technologists that apply their passion and expertise to solving the most nuanced problems in our industry. 

What to Expect

  • Use advanced research experience and expertise to apply academic research to impactful real-world problems in trading across time horizons and machine learning strategies
  • Leverage our proprietary infrastructure (Python/C++) in conjunction with third-party tools to conduct quantitative research and data analysis
  • Use machine learning and time series techniques to derive novel insights on market behavior from large and complex datasets
  • Utilize our industry-leading compute cluster to run simulations and crunch data
  • Build predictive models for financial markets using a combination of market and non-market data
  • Attend and participate in Tech Talks that provide an overview of markets and HRT's trading philosophy
  • Enjoy a curriculum of speakers, trading games, mentorships, and social events throughout the summer

Qualifications

  • You are a full-time PhD student in a quantitative discipline (math, physics, computer science, statistics, operations research, machine learning etc.)
  • Fluency in Python is a must
  • Experience with statistical analysis, numerical programming, or machine learning in Python, Pandas/Numpy, R, and/or MATLAB
  • You're excited to apply your research expertise to identify new opportunities in worldwide markets  
    Strong communication skills

We offer a weekly base salary offer in addition to a competitive signing bonus, company-paid housing, meals, and other perks.

New York: Weekly base salary of 5,800 USD
Singapore: Weekly base salary of 7,650 SGD
London: Weekly base salary of 4,350 GBP

We do not allow multiple applications. Please apply to the ONE role you are most interested in and we will consider you for all open positions when reviewing your application.

Apply