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Deep Learning Quantization Jobs in Texas (NOW HIRING)

Nvidia

Senior Software Engineer - Local AI

Austin, TX

$121K - $160K/yr

More recently, GPU deep learning ignited modern AI - the new era of computing, positioning GPUs as ... quantization, distillation, and pruning-for large AI models; fine-tuning and compressing models to ...

T3i, Inc.

Computer Vision Engineer - UAS

San Antonio, TX

$100K - $118K/yr

... quantization, pruning, and distillation techniques * Write production-quality C++ and Python ... Solid foundation in modern deep learning architectures for vision (CNNs, transformers, detection ...

webAI

AI Software Engineer

Austin, TX · On-site

The AI Software Engineer will focus on optimizing machine learning models for efficiency and ... Implement techniques like pruning, quantization, and distillation to ensure models run efficiently ...

CCS INC

Lead Gen AI Engineer

Plano, TX · On-site

$85 - $110/hr

Deep understanding of LLMs, embeddings, vector databases (e.g., FAISS, Pinecone, Weaviate ... Use techniques like quantization, distillation, and caching to improve efficiency.

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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

Senior AI Software Architect - Autonomous Systems

Advanced Micro Devices, Inc

Austin, TX • On-site

$147K/yr

Full-time

Re-posted 8 days ago

Advanced Micro Devices rating

8.6

Company rating: 8.6 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

27th of 156 rated electronics manufacturers

Job description

WHAT YOU DO AT AMD CHANGES EVERYTHING
At AMD, our mission is to build great products that accelerate next-generation computing experiences-from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you'll discover the real differentiator is our culture. We push the limits of innovation to solve the world's most important challenges-striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.
About the Role
We are seeking a highly skilled Senior AI Software Engineer to join our team. The ideal candidate will be responsible for designing and implementing AI-driven software solutions tailored for autonomous systems. This role requires deep expertise in AI frameworks, model analysis and optimization, GPU-accelerated computing, and system performance profiling within high-performance autonomous environments.
Key Responsibilities
  • Select and optimize AI models for autonomous applications, ensuring scalability, low latency, and real-time performance across edge and cloud deployments.
  • Collaborate with cross-functional teams to ensure seamless integration of AI frameworks into software stacks, optimizing inference pipelines and model performance for real-world use cases.
  • Provide technical leadership and guidance in AI software best practices, focusing on deep learning frameworks, GPU-accelerated computing, model compression, and efficient deployment strategies for autonomous systems.
  • Analyze and optimize AI software stack performance, particularly in real-time inference, GPU-accelerated, and autonomous navigation environments, identifying bottlenecks and implementing targeted improvements.
  • Stay updated with the latest trends and technologies in AI frameworks, foundation models, edge AI deployment and autonomous systems integration, offering insights and recommendations to continuously advance AI capabilities.

Preferred Experience
  • AI Frameworks: Proven experience designing and implementing solutions using leading AI frameworks such as PyTorch, TensorFlow, JAX, or ONNX Runtime, with a focus on autonomous applications.
  • AI Model Analysis & Optimization: Strong knowledge and hands-on experience with model profiling, benchmarking, quantization, pruning, and distillation techniques to optimize AI models for performance and efficiency.
  • ROCm, CUDA & GPU Computing: Deep expertise in ROCm or CUDA programming, GPU kernel optimization, and GPU memory management for accelerating AI inference and training workloads.
  • System Performance Analysis: Expertise in profiling and analyzing end-to-end AI system performance using tools such as NVIDIA Nsight, TensorRT, Triton Inference Server, or similar profiling and optimization platforms.
  • Autonomous Systems: Experience deploying AI models within software stacks, including integration with ROS/ROS2, real-time systems, and edge AI hardware platforms (NVIDIA Jetson, etc.).
  • Problem-Solving Skills: Excellent problem-solving skills and attention to detail in debugging complex AI model behavior, performance regressions, and hardware-software interactions.
  • Collaboration and Communication: Ability to work collaboratively in a cross-functional team environment with strong written and verbal communication skills.

ACADEMIC CREDENTIALS:
  • Bachelor's or Master's in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field

Location
  • Austin, TX
  • Other locations are considered for exceptional candidates.

This role is not eligible for visa sponsorship.
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Benefits offered are described: AMD benefits at a glance.
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD's "Responsible AI Policy" is available here.
This posting is for an existing vacancy.

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