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Machine Learning Engineer Quantization Jobs in Sunnyvale, CA

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine ... Hands on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF)

... Machine Learning Engineer to translate cutting-edge research into scalable, production-ready ... distillation, quantization, deployment optimization). • Experienced in inference time ...

They are seeking a Machine Learning Engineer to translate research into scalable solutions ... distillation, quantization, deployment optimization). • Experienced in inference time ...

Optimize models for production deployment, including ONNX / TensorRT / quantization / inference ... Electrical Engineering, Robotics, Computer Vision, Machine Learning, or a related field. * 3-5 ...

Optimize models for production deployment, including ONNX / TensorRT / quantization / inference ... Electrical Engineering, Robotics, Computer Vision, Machine Learning, or a related field. * 3-5 ...

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift: N/A Location: San Mateo, CA (Peninsula) - Onsite Preferred Schedule: Full time, Permanent Role Visa ...

About the role: We're looking for an early career Machine Learning Engineer to join our team. In this role you will build and deploy state of the art machine learning models to solve complex ...

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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 20, 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:

Staff Machine Learning Engineer - Model Optimization & Quantization

Socket.dev

Santa Clara, CA • On-site

$161 - $241/hr

Other

Posted 6 days ago


Job description

Company:

Qualcomm Technologies, Inc.

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary: About the Role

Join the Qualcomm AI Hub team and help developers integrate machine learning into their products and experiences: https://aihub.qualcomm.com/.

Inthisroleyou willdevelop tools tohelp developersoptimizeand deploy machine learning models on edge and mobile hardware. AIMETis Qualcomm'sopen-source library forstate-of-the-artmodel quantization, and compression techniques. You will develop and supportcutting-edgemodel optimization workflows — pushing the boundary ofwhat'spossible on resource-constrained hardware. Applications range from quantizing large language models (LLMs) and generative AI models to compressing latency-critical vision, audio, and multimodal networks for deployment on Qualcomm Snapdragon and other edge SoCs.

For this role we areseekinga talented and motivated Staff Software Engineer withexpertiseintheoptimizingand deployingML models– especially for edge devices.

What You'll Do
  • Design, develop, and maintain quantization algorithms and compression pipelines within the AIMET framework (PTQ, QAT, mixed-precision, AdaScaleetc.)

  • Implement advanced quantization techniques including weight-only quantization, activation quantization, KV-cache quantization, and sub-4-bit quantization for LLMs and generative AI models

  • Build tooling to analyze, profile, and debug model accuracy degradation caused by quantization

  • Integrate AIMET workflows with popular ML frameworks —PyTorchand ONNX

  • Develop APIs and developer-facing tooling to make AIMET accessible and easy to use for external customers and design partners

  • Integrate AIMET in AI Hub Workbench Quantize job to enable Quantization at large scale.

  • Own end-to-end quantization and optimization of models published on Qualcomm AI Hub, ensuring they meet accuracy, latency, and power targets on Qualcomm hardware

  • Quantize and validate a broad range of model families — vision transformers, LLMs, diffusion models, speech, and multimodal architectures — for deployment via AI Hub

  • Develop and maintain automated quantization pipelines and evaluation harnesses to scale model onboarding across AI Hub's growing model catalog

Minimum Qualifications:
  • Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
  • Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
  • PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
Preferred Qualifications:
  • 3+ years of industry experience in machine learning, deep learning, or AI infrastructure

  • Strongproficiencyin Python, with hands-on experience inPyTorch, ONNX and/or TensorFlow

  • Solid understanding of neural network architectures — CNNs, Transformers, LLMs, diffusion models, multimodal models

  • Experience with model quantization techniques — PTQ, QAT, weight-only quantization, mixed-precision, sub-4-bit methods

  • Hands-on experience quantizing LLMs (GPT,LLaMA, Mistral, Falcon, or similar families) for inference optimization

  • Familiarity with AIMET, GPTQ, AWQ,SmoothQuant, or similar quantization frameworks is a strong plus

  • Experience working with ONNX,TFLite/LiteRT, or other model interchange formats

  • Understanding of hardware constraints: memory bandwidth, compute precision (INT4/INT8/FP16/BF16), and NPU/DSP execution

  • Experience collaborating across teams or BUs to drive technical alignment and model delivery

  • Proficiencywith git and software development best practices

  • Strong written and verbal communication skills — ability to write clean APIs, documentation, and engage directly with external developers

  • Experience with C++ for performance-critical components is a bonus

  • Familiarity with ARM processors and mobile SoC architecture (Snapdragon) is a plus

  • Experience with automated evaluation pipelines and model benchmarking at scale is a plus

Level of Responsibility
  • Works independently with minimal supervision

  • Provides technical guidance and mentorship to other team members

  • Decision-making is significant and affects work beyond the immediate team

  • Requiresstrong communicationskills to convey complex quantization concepts to varied audiences — from hardware engineers and BU partners to external researchers and application developers

  • Has meaningful influence on the AIMET product roadmap, AI Hub model catalog, and cross-BU quantization strategy

  • Tasks are open-ended; planning, prioritization, and problem-solving are core to the role

Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.

Pay range and Other Compensation & Benefits

$160,500.00 - $240,700.00

The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link.

If you would like more information about this role, please contact Qualcomm Careers.

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