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Deep Learning Quantization Jobs in Santa Clara, CA

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine ... quantization, deployment optimization). * Experienced in inference time optimization, deep ...

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

Nace AI is a company focused on machine learning solutions, and they are seeking a Machine Learning ... quantization, deployment optimization). • Experienced in inference time optimization, deep ...

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

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

Apptronik

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$122K - $167K/yr

... deep learning approaches. • Expertise in model acceleration, quantization, or compression (TensorRT, ONNX Runtime). • Familiarity with real-time frameworks and middleware such as ROS 2, GStreamer ...

Apptronik

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$122K - $168K/yr

Strong classical computer vision skills (geometry-based methods, feature extraction) complementing deep learning approaches. * Expertise in model acceleration, quantization, or compression (TensorRT ...

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Showing results 21-40

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Santa Clara, CA

Deep Learning Quantization information

See Santa Clara, CA salary details

$12.9K

$98.5K

$164.4K

How much do deep learning quantization jobs pay per year?

As of Aug 8, 2026, the average yearly pay for deep learning quantization in Santa Clara, CA is $98,518.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,600.00 and $163,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a deep learning quantization engineer, and why are they important?

To excel as a Deep Learning Quantization Engineer, you need a strong background in machine learning, applied mathematics, and computer science, usually supported by an advanced degree in a related field. Familiarity with deep learning frameworks (such as TensorFlow or PyTorch), quantization toolkits, and hardware acceleration platforms is crucial. Analytical thinking, problem-solving, and clear technical communication are standout soft skills in this role. These abilities are essential for efficiently optimizing models for deployment on resource-constrained hardware while maintaining accuracy and performance.

What is the difference between Deep Learning Quantization vs Machine Learning Engineer?

AspectDeep Learning QuantizationMachine Learning Engineer
Required CredentialsAdvanced degrees in AI, Computer Science, or related fields; knowledge of neural networksBachelor's or Master's in CS, Data Science, or related fields; programming skills
Work EnvironmentResearch labs, AI development teams, hardware optimization settingsSoftware development teams, data-driven projects, product-focused environments
Industry UsageAI hardware optimization, model deployment, edge computingModel development, data analysis, software solutions across industries

Deep Learning Quantization focuses on reducing model size and improving inference speed through techniques like weight and activation quantization, often in hardware or embedded systems. Machine Learning Engineers develop, implement, and optimize machine learning models for various applications. While both roles require knowledge of AI and programming, Deep Learning Quantization is more specialized in model optimization techniques, whereas Machine Learning Engineers work broadly on model development and deployment.

What is deep learning quantization?

Deep learning quantization is the process of reducing the precision of the numbers used to represent a neural network's parameters, activations, or both. By converting the typically used 32-bit floating-point values to lower bit-width formats such as 16-bit or 8-bit integers, quantization significantly reduces the memory footprint and computational requirements of deep learning models. This technique helps deploy models efficiently on edge devices and mobile hardware while maintaining acceptable accuracy levels. Quantization is widely used in model optimization for faster inference and lower power consumption.

What are some common challenges faced when implementing deep learning quantization in production environments?

One of the main challenges in implementing deep learning quantization is balancing model accuracy with computational efficiency, as quantization can sometimes lead to a drop in model performance. Additionally, ensuring hardware compatibility and optimizing for different devices (such as CPUs, GPUs, or edge devices) can require extensive testing and tuning. Collaboration with data scientists, software engineers, and hardware specialists is often essential to successfully deploy quantized models at scale. Staying updated with the latest quantization techniques and frameworks is also important for overcoming these challenges.
What are popular job titles related to Deep Learning Quantization jobs in Santa Clara, CA? For Deep Learning Quantization jobs in Santa Clara, CA, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Santa Clara, CA look for? The top searched job categories for Deep Learning Quantization jobs in Santa Clara, CA are:
What cities near Santa Clara, CA are hiring for Deep Learning Quantization jobs? Cities near Santa Clara, CA with the most Deep Learning Quantization job openings:
Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in Santa Clara, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $98,518 per year, or $47.4 per hour.

Machine Learning Engineer

Nace AI

Palo Alto, CA • On-site

Full-time

Re-posted 20 days ago

Job description

Role Overview:
As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross-functional teams to identify opportunities where ML can drive product value, architect robust model-centric systems, and ensure their seamless integration into real-world applications. The role requires a strong balance between theoretical understanding and engineering execution, with a focus on building reliable, maintainable, and high-impact AI-driven features that align with Nace.AI's strategic objectives.
Key Responsibilities:
  • Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance evaluation.
  • Fine-tune large language models (LLMs) and implement meta-learning methods to enhance model generalization and efficiency.
  • Improve existing Nace.AI models by incorporating advancements from recent ML research.

Qualifications:
  • Hands-on experience training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical work with pre-training, instruction tuning, and alignment techniques (GRPO,RLHF/DPO/PPO).
  • Hands-on Experience with Deep Learning Models, especially Transformers.
  • Ability to translate cutting-edge research from papers into clean, production-ready code (Paper to Code).
  • Proven experience scaling inference infrastructure for LLMs/VLMs, including expertise in model serving frameworks like vLLM, TGI.
  • Proficient in Python with a strong track record of building substantial projects.
  • Solid foundation in computer science fundamentals (data structures, algorithms, design patterns).
  • BS degree in CS or related technical field.
  • Solid Experience with ML frameworks and libraries (PyTorch, TensorFlow).
  • Self-starter comfortable working in a fast-paced, dynamic environment.

Preferred Qualifications:
  • MS/PhD in CS or related technical field.
  • Familiarity with data processing stacks such as Spark and Airflow.
  • Experience with multi-node GPU training.
  • Contributor to open-source ML projects.
  • Deep knowledge in Linear Programming.
  • Experience with advanced NLP and Multimodal post-training experience (e.g., model distillation, quantization, deployment optimization).
  • Experienced in inference time optimization, deep understanding of LLM serving optimizations for LLMs/VLMs.
  • Hands on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF).

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