... Learning Engineer to join their AI Hub team. The role involves developing tools for optimizing and deploying machine learning models on edge and mobile hardware, focusing on model quantization and ...
... Learning Engineer to join their AI Hub team. The role involves developing tools for optimizing and deploying machine learning models on edge and mobile hardware, focusing on model quantization and ...
Machine Learning Engineer
Palo Alto, CA · On-site
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
Palo Alto, CA · On-site
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)
Improve inference efficiency and model compression techniques, including quantization, pruning, and ... Engineering, Machine Learning, or related fields. * Must have prior experience managing a team ...
Improve inference efficiency and model compression techniques, including quantization, pruning, and ... Engineering, Machine Learning, or related fields. * Must have prior experience managing a team ...
They are looking for passionate machine learning engineers to develop robotics technology and shape ... quantization, etc.) that can be deployed locally in humanoid robots. • Work on efficient and ...
They are looking for passionate machine learning engineers to develop robotics technology and shape ... quantization, etc.) that can be deployed locally in humanoid robots. • Work on efficient and ...
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 ...
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 ...
With this mission, we are looking for passionated machine learning engineers of all levels who will ... Work on efficient foundation models (e.g. small LLMs, weight sharing, model quantization, etc ...
With this mission, we are looking for passionated machine learning engineers of all levels who will ... Work on efficient foundation models (e.g. small LLMs, weight sharing, model quantization, etc ...
With this mission, we are looking for passionated machine learning engineers of all levels who will ... Work on efficient foundation models (e.g. small LLMs, weight sharing, model quantization, etc ...
With this mission, we are looking for passionated machine learning engineers of all levels who will ... Work on efficient foundation models (e.g. small LLMs, weight sharing, model quantization, etc ...
Senior Machine Learning Engineer
$200K - $280K/yr
Improve inference efficiency and model compression techniques, including quantization, pruning, and ... Engineering, Machine Learning, or related fields. * Must have prior experience managing a team ...
Senior Machine Learning Engineer
$200K - $280K/yr
Improve inference efficiency and model compression techniques, including quantization, pruning, and ... Engineering, Machine Learning, or related fields. * Must have prior experience managing a team ...
Senior Machine Learning Engineer
San Jose, CA · On-site
$200K - $280K/yr
Improve inference efficiency and model compression techniques, including quantization, pruning, and ... Engineering, Machine Learning, or related fields. * Must have prior experience managing a team ...
Senior Machine Learning Engineer
San Jose, CA · On-site
$200K - $280K/yr
Improve inference efficiency and model compression techniques, including quantization, pruning, and ... Engineering, Machine Learning, or related fields. * Must have prior experience managing a team ...
Machine Learning Engineer
San Francisco, CA · On-site
$160K - $220K/yr
Optimize inference, batching, and quantization on GPU * Productionize models with clear SLAs and ... Solid engineering discipline and metrics focus Nice to have * Triton Inference Server, TensorRT ...
Machine Learning Engineer
San Francisco, CA · On-site
$160K - $220K/yr
Optimize inference, batching, and quantization on GPU * Productionize models with clear SLAs and ... Solid engineering discipline and metrics focus Nice to have * Triton Inference Server, TensorRT ...
Machine Learning Engineer
Fremont, CA · On-site
Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only h1 candidate About the Role: Our direct client is hiring a Machine Learning Engineer for their ...
Quick apply
Machine Learning Engineer
Fremont, CA · On-site
Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only h1 candidate About the Role: Our direct client is hiring a Machine Learning Engineer for their ...
Tesla is seeking a highly skilled Machine Learning Engineer to join the Supply Chain Engineering ... with quantization-aware training, model compression, and distillation for edge and real-time ...
Tesla is seeking a highly skilled Machine Learning Engineer to join the Supply Chain Engineering ... with quantization-aware training, model compression, and distillation for edge and real-time ...
Machine Learning Engineer
San Mateo, CA · On-site
Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...
Machine Learning Engineer
San Mateo, CA · On-site
Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...
Machine Learning Engineer
Fremont, CA · On-site
Machine Learning Engineer Location: Fremont, CA once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a ...
Quick apply
Machine Learning Engineer
Fremont, CA · On-site
Machine Learning Engineer Location: Fremont, CA once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a ...
Machine Learning Engineer
San Mateo, CA · On-site +1
Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...
Machine Learning Engineer
San Mateo, CA · On-site +1
Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...
Machine Learning Engineer Location: San Francisco, CA Sponsorship: No Relocation: No Industry: Machine Learning Join an artificial intelligence company in San Francisco that excels at visual ...
Quick apply
Machine Learning Engineer Location: San Francisco, CA Sponsorship: No Relocation: No Industry: Machine Learning Join an artificial intelligence company in San Francisco that excels at visual ...
Machine Learning Engineer
Cupertino, CA · On-site
As a Machine Learning Engineer, you will design and build cutting-edge AI/ML systems that drive meaningful business outcomes at scale. You will work cross-functionally to bring innovative machine ...
Machine Learning Engineer
Cupertino, CA · On-site
As a Machine Learning Engineer, you will design and build cutting-edge AI/ML systems that drive meaningful business outcomes at scale. You will work cross-functionally to bring innovative machine ...
Machine Learning Engineer
San Jose, CA · On-site
Machine Learning Engineer The Opportunity Join Adobe and be at the forefront of driving digital transformation. As a Machine Learning Engineer, you will play a key role in developing machine learning ...
Machine Learning Engineer
San Jose, CA · On-site
Machine Learning Engineer The Opportunity Join Adobe and be at the forefront of driving digital transformation. As a Machine Learning Engineer, you will play a key role in developing machine learning ...
... quantization, pruning, converting to ONNX/TensorRT, custom GPU kernels and profiling. * Help ... machine learning fundamentals. * Strong software engineering fundamentals including testing ...
... quantization, pruning, converting to ONNX/TensorRT, custom GPU kernels and profiling. * Help ... machine learning fundamentals. * Strong software engineering fundamentals including testing ...
Machine Learning Engineer Quantization information
See Sunnyvale, CA salary details
$37K - $54.3K
1% of jobs
$54.3K - $71.5K
1% of jobs
$71.5K - $88.8K
5% of jobs
$88.8K - $106.1K
6% of jobs
$120.4K is the 25th percentile. Wages below this are outliers.
$106.1K - $123.4K
14% of jobs
$123.4K - $140.7K
14% of jobs
The median wage is $149.3K / yr.
$140.7K - $158K
18% of jobs
$158K - $175.2K
14% of jobs
$178.8K is the 75th percentile. Wages above this are outliers.
$175.2K - $192.5K
12% of jobs
$192.5K - $209.8K
11% of jobs
$209.8K - $227.1K
5% of jobs
$37K
$151.1K
$227.1K
How much do machine learning engineer quantization jobs pay per year?
As of Jul 8, 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 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?
| Aspect | Machine Learning Engineer Quantization | Data Scientist |
|---|---|---|
| Required Credentials | Bachelor's or master's in CS, ML, or related; certifications in ML or AI | Bachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics |
| Work Environment | Developing optimized ML models, deploying quantized models for efficiency | Analyzing data, building predictive models, interpreting results |
| Industry Usage | Tech companies, AI hardware firms, embedded systems | Finance, 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 job categories do people searching Machine Learning Engineer Quantization jobs in Sunnyvale, CA look for? The top searched job categories for Machine Learning Engineer Quantization jobs in Sunnyvale, CA are:
- Artificial Intelligence Machine Learning Engineer
- Llm Training
- Reinforcement Learning
- Machine Learning Engineer Hybrid
- Machine Learning Engineer Opt
- Reinforcement Learning Engineer
- Weekend Machine Learning
- Seasonal Medical Imaging Machine Learning
- Machine Learning Biomedical Engineer
- Machine Learning Engineer Two
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
Santa Clara, CA • On-site
Full-time
Re-posted 27 days ago
Qualcomm rating
9.6
Based on 5 frontline employees who took The Breakroom Quiz
5th of 204 rated software companies
Job description
Job Summary:
Qualcomm Technologies, Inc. is seeking a Staff Machine Learning Engineer to join their AI Hub team. The role involves developing tools for optimizing and deploying machine learning models on edge and mobile hardware, focusing on model quantization and compression techniques.
Responsibilities:
• Design, develop, and maintain quantization algorithms and compression pipelines within the AIMET framework (PTQ, QAT, mixed-precision, AdaScale etc.)
• 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 — PyTorch and 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
Qualifications:
Required:
• 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.
• OR 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.
• OR 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:
• 3+ years of industry experience in machine learning, deep learning, or AI infrastructure
• Strong proficiency in Python, with hands-on experience in PyTorch, 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
• Proficiency with 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
Company:
Qualcomm designs wireless technologies and semiconductors that power connectivity, communication, and smart devices. Founded in 1985, the company is headquartered in San Diego, USA, with a team of 10001+ employees. The company is currently Late Stage.
Qualcomm Technologies, Inc. is seeking a Staff Machine Learning Engineer to join their AI Hub team. The role involves developing tools for optimizing and deploying machine learning models on edge and mobile hardware, focusing on model quantization and compression techniques.
Responsibilities:
• Design, develop, and maintain quantization algorithms and compression pipelines within the AIMET framework (PTQ, QAT, mixed-precision, AdaScale etc.)
• 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 — PyTorch and 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
Qualifications:
Required:
• 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.
• OR 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.
• OR 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:
• 3+ years of industry experience in machine learning, deep learning, or AI infrastructure
• Strong proficiency in Python, with hands-on experience in PyTorch, 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
• Proficiency with 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
Company:
Qualcomm designs wireless technologies and semiconductors that power connectivity, communication, and smart devices. Founded in 1985, the company is headquartered in San Diego, USA, with a team of 10001+ employees. The company is currently Late Stage.
About Qualcomm
Sourced by ZipRecruiter
Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including 5G-enabled smartphones that double as pro-level cameras and gaming devices, smarter vehicles and cities, and the technology behind the smart, connected factories that manufactured your latest purchase. Our powerful connectivity solutions keep you connected—even in remote areas. Qualcomm 5G and AI innovations are the power behind the connected intelligent edge. You’ll find our technologies behind and inside the innovations that deliver significant value across multiple industries and to billions of people every day.
Industry
Technology, communication and media
Company size
10,000+ Employees
Headquarters location
San Diego, CA, US
Year founded
1985