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Machine Learning Engineer Quantization Jobs in Sunnyvale, CA

Machine Learning Engineer

San Francisco, CA ยท On-site

$200K - $280K/yr

We're looking for an exceptional Machine Learning Engineer to help build the systems that make this possible. In this role, you'll develop models, signals and evaluation frameworks that power ...

Position Overview We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and ...

Machine Learning Engineer

San Francisco, CA ยท On-site

$225K - $300K/yr

Machine Learning Engineer About Latent Health Healthcare today is only truly personalized for two groups: those with wealth and access, and those with physicians in their immediate family. For ...

Machine Learning Engineer

San Mateo, CA ยท On-site

$100K - $300K/yr

Position Overview We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and ...

BeeGenius is building the future of work, and they are seeking an AI/Machine Learning Engineer to join their team. In this role, you will be responsible for developing and implementing machine ...

Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple ...

Who We're Looking For As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple ...

Showing results 41-60

Machine Learning Engineer Quantization information

See Sunnyvale, CA salary details

$37K

$151.1K

$227.1K

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

As of Aug 21, 2026, the average yearly pay for machine learning engineer quantization in Sunnyvale, CA is $151,130.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,100.00 and $181,900.00 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 are popular job titles related to Machine Learning Engineer Quantization jobs in Sunnyvale, CA?

For Machine Learning Engineer Quantization jobs in Sunnyvale, CA, the most frequently searched job titles are:

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

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

Machine Learning Engineer

RZR Global Inc.

San Francisco, CA โ€ข On-site

Full-time

Re-posted 3 days ago


Job description

Who are we?RZR Global is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC.
Role Overview
We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will be involved in designing and implementing machine learning models and data pipelines to enhance our programmatic demand-side platform (DSP). You will work closely with Senior MLE and other team members to drive impactful machine learning projects and contribute to innovative solutions.
Key Responsibilities
  • Support the development of machine learning models to address challenges in programmatic advertising, such as predicting user responses, forecasting bid landscapes, and detecting fraud.
  • Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.
  • Analyze the impact of integrating new data sources and features into our models.
  • Build and maintain data pipelines to process and prepare large datasets for model training and evaluation.
  • Contribute ideas and assist in testing new tools, methodologies, and technologies to improve our machine learning capabilities.
  • Document experiments, assumptions, and outcomes; maintain reproducibility
Required Skills / Experience
  • Bachelor's or Master's degree in Mathematics, Physics, Computer Science, or a related technical field.
  • At least 1 year of professional experience in machine learning, statistical analysis, and data analysis.
  • Experience with machine learning techniques such as regression, classification, and clustering.
  • Proficiency in Python and SQL and familiarity with big data tools (e.g., Spark) and ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
  • Strong grasp of probability, statistics, and data analysis principles.
  • Ability to work effectively in a team environment, with good communication skills to explain complex concepts to diverse stakeholders.
Nice-to-Have
  • Familiarity with system programming languages including C++ and Rust is a plus.
  • Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)
  • Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.