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Deep Learning Quantization Jobs in Irving, TX (NOW HIRING)

Burnetts Staffing

Computer Vision Engineer

Fort Worth, TX

$43.27 - $61/hr

Proficiency in Python and deep learning frameworks (PyTorch or TensorFlow) * Experience designing ... Familiarity with inference optimization (TensorRT, ONNX, quantization techniques) * Ability to work ...

Burnetts Staffing

Computer Vision Engineer

Fort Worth, TX

$43.27 - $61/hr

Proficiency in Python and deep learning frameworks (PyTorch or TensorFlow) * Experience designing ... Familiarity with inference optimization (TensorRT, ONNX, quantization techniques) * Ability to work ...

CCS INC

Lead Gen AI Engineer

Plano, TX

$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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All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Irving, TX

Deep Learning Quantization information

See Irving, TX salary details

$10.6K

$80.6K

$134.4K

How much do deep learning quantization jobs pay per year?

As of Jun 28, 2026, the average yearly pay for deep learning quantization in Irving, TX is $80,551.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,100.00 and $133,500.00 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.
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Deep Learning Quantization jobs near you
SR MACHINE LEARNING EMBEDDED ENGINEER

SR MACHINE LEARNING EMBEDDED ENGINEER

Software Technology Inc

Plano, TX • On-site

$119K - $156K/yr

Other

Posted 22 days ago

Job description

Sr Machine Learning Engineer

The client's Mobility team is responsible for building and managing our connected vehicle platforms, supporting product research using vehicle sensor data, and creating new and exciting data services for client-customers that make driving safer, more convenient, and fun. We’re looking for a Sr Machine Learning Engineer capable of using machine learning and statistical techniques to create state-of-the-art solutions for non-trivial, and arguably, unsolved problems. If you are results-driven, interested in how to apply advanced machine learning techniques, would love to work with vehicle telemetry data and video, are deeply technical, highly innovative, and long for the opportunity to build solutions for challenging problems that directly impact the company's bottom-line, we want to talk to you.

Responsibilities
  • Use statistical and machine learning techniques to create scalable solutions for vehicle telemetry data and video analysis, and perform R&D to drive the discovery of new-generation mobility products
  • Establish scalable, efficient, automated processes for large-scale data analysis, model development, model validation and model implementation
  • Develop ML models to run in vehicle (Edge)
  • Develop and deploy CV models on Edge
  • Drive adoption of best practices across organizations
  • Deliver production-ready code
  • Work with Product Owners to define the KPIs for machine learning projects
  • Stay abreast of developments in research methodology and changing technologies in the marketplace and proactively identify applications of these latest developments to improve existing methods
  • Prepare and present findings to both technical and non-technical audiences
  • Work within the constraints of time, budget, and resources capacities to align with the client's global vision
  • Develop and foster collaborative relationships with product, business, and engineering teams to effectively serve our customer needs
Qualifications
  • 5+ years of production experience working in Data Science or Software Engineering
  • 3+ years of production experience in Deep Learning - Computer Vision
  • Solid production experience using Python (including NumPy), C/ C++, Lua and SQL
  • Experience in embedded systems development and troubleshooting and with real-time operating systems
  • Experience with CNNs and other types of neural networks in machine learning, or Robotics, or AI
  • Experience in neural network quantization, compression, and algorithm pruning
  • Application layer development and optimization of deep learning algorithms in embedded systems
  • Experience with C++ development in embedded applications
  • Experience with common embedded operating systems and environments such as Linux, etc.
  • Solid production experience using TensorFlow and/or PyTorch
  • Production experience with Apache Spark
  • Experience implementing solutions for video and image segmentation, object detection and tracking, and/or semantic/instance segmentation
  • Strong fundamentals in problem solving, algorithm design and complexity analysis
  • Experience implementing and orchestrating Machine Learning pipelines in production environments, using tools such as Kubeflow, airflow, Pachyderm, mlflow, etc.

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