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Deep Learning Quantization Jobs (NOW HIRING)

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 ...

EnCharge AI

$175K - $245K/yr

Research and develop quantization-aware training (QAT) and post-training quantization (PTQ) techniques for deep learning models. * Implement low-bit precision optimizations (e.g., INT8, BF16)

XPENG

AI Intern - VLA Deployment

Santa Clara, CA

... deep learning systems, model deployment, and edge inference for real-world autonomous driving applications. Key Responsibilities * Support model quantization and deployment efforts for large-scale ...

XPENG

AI Intern - VLA Deployment

Santa Clara, CA · On-site

... deep learning systems, model deployment, and edge inference for real-world autonomous driving applications. Key Responsibilities * Support model quantization and deployment efforts for large-scale ...

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 ...

Rivian

Machine Learning/AI Engineer

Palo Alto, CA · On-site

$139K - $155K/yr

Role Summary We are looking for a Research Scientist with deep expertise in quantized deep learning ... Design and implement hardware-aware optimizations, including quantization strategies, model ...

Apptronik

Senior Perception Learning Engineer

Sunnyvale, CA · On-site

$190K - $235K/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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All JobsDeep Learning Quantization Jobs

Deep Learning Quantization information

See salary details

$11K

$83.9K

$140K

How much do deep learning quantization jobs pay per year?

As of Jul 14, 2026, the average yearly pay for deep learning quantization in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 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.
More about Deep Learning Quantization jobs
What cities are hiring for Deep Learning Quantization jobs? Cities with the most Deep Learning Quantization job openings:
What states have the most Deep Learning Quantization jobs? States with the most job openings for Deep Learning Quantization jobs include:
What job categories do people searching Deep Learning Quantization jobs look for? The top searched job categories for Deep Learning Quantization jobs are:
Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 25% Part Time, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Machine Learning Engineer

Nace AI

Palo Alto, CA • On-site

Full-time

Re-posted 26 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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