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Deep Learning Quantization Jobs in California (NOW HIRING)

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Deep Learning Quantization information

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.
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Deep Learning Quantization jobs near you
Infographic showing various Deep Learning Quantization job openings in California as of July 2026, with employment types broken down into 74% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution.
Senior Software Engineer, Deep Learning - MLIR TRT

Senior Software Engineer, Deep Learning - MLIR TRT

NVIDIA

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Re-posted yesterday

Nvidia rating

9.3

Company rating: 9.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

15th of 209 rated software companies

Job description

Job Summary:
NVIDIA has been a leader in computer graphics and accelerated computing for over 25 years and is now focusing on AI to revolutionize computing. They are seeking a Senior Deep Learning Software Engineer to develop and productize deep learning solutions for autonomous vehicles, including optimizing compiler technology for NVIDIA's hardware architecture.
Responsibilities:
• Developing compiler technologies to accelerate deep learning inference on NVIDIA hardware platforms for Physical AI.
• Working across a wide range of abstractions from model fine-tuning and quantization to low-level kernel development and performance optimization.
• Develop workflows that let users leverage frameworks (e.g. PyTorch, JAX) and compiler technologies tools (e.g. MLIR, Triton) without forgoing performance
• Work with customers to help accelerate their workloads on NVIDIA platforms.
• Stay up to date with the latest research and innovations in deep learning, implement and experiment with new insights to improve NVIDIA's Physical AI DNNs.
Qualifications:
Required:
• MS or PhD degree in computer science, computer vision, robotics, computer architecture or equivalent experience in technical field (or equivalent experience)
• 5+ years of work experience in software development.
• 2+ years of experience in developing deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc.) or compiler technologies (e.g. LLVM, MLIR, TVM, Triton, etc.).
• Domain experience in technologies used for GPU programming (e.g. CUDA C++ and/or DSLs like OpenAI Triton) or with system-level optimization for deep learning training or inference.
• Strong C/C++ programming skills
• Familiar with start-of-the-art deep learning techniques for inference and training.
• Willing to take action and have strong analytical skills.
Preferred:
• Experience with MLIR or LLVM or similar compiler technologies
• Background with low precision inference, quantization, compression of DNNs
• Experience with GPU programming
• Experience with building DSLs or optimizing compilers (e.g. graph compiler or kernel generator) for GPUs or other accelerated computing platforms.
• Open source project ownership or contribution, healthy GitHub repositories, guiding and/or mentoring experience
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.

What Nvidia employees say

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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