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

Diverse Lynx

AI/ ML Engineer

Irving, TX

$120K - $125K/yr

Strong grasp of deep learning architectures including CNNs, RNNs, Transformers, and multimodal ... post training quantization, ONNX, CoreML, and edge deployment. * Understanding of software ...

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 Dallas, TX

Deep Learning Quantization information

See Dallas, TX salary details

$10.9K

$83K

$138.5K

How much do deep learning quantization jobs pay per year?

As of Jun 20, 2026, the average yearly pay for deep learning quantization in Dallas, TX is $82,982.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,200.00 and $137,500.00 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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What cities near Dallas, TX are hiring for Deep Learning Quantization jobs? Cities near Dallas, TX with the most Deep Learning Quantization job openings:
Deep Learning Quantization jobs near you
AI/ ML Engineer

AI/ ML Engineer

Diverse Lynx

Irving, TX

$120K - $125K/yr

Other

Posted 13 days ago

Job description

AI/ML Engineer

Location: Irving, TX

Salary Range: $120,000 - $125,000 a year (plus full time benefits)

Job Description
  • 5+ years of hands-on experience in Machine Learning, Deep Learning, or related domains.
  • Proven experience building, scaling, and deploying ML models in production environments with measurable impact.
  • Strong proficiency in PyTorch (preferred) or TensorFlow, and Python-based ML development.
  • Solid foundations in mathematical modeling, including Linear Algebra, Probability, Statistics, and optimization theory.
  • Deep understanding of core ML principles: model training, validation, generalization, and performance trade-offs.
  • Strong grasp of deep learning architectures including CNNs, RNNs, Transformers, and multimodal models. Clear understanding of when and why to apply each architecture for spatial, temporal, or cross-modal tasks.
  • Practical experience in computer vision tasks such as object detection, segmentation, tracking, video understanding, or temporal modeling.
  • Familiarity with model optimization and deployment techniques including pre/post training quantization, ONNX, CoreML, and edge deployment.
  • Understanding of software engineering guidelines including version control (Git), testing frameworks, CI/CD, and code review processes.
  • Strong analytical thinking, problem-solving ability, and curiosity to learn in a fast-evolving AI landscape.
  • Excellent communication skills and the ability to collaborate effectively within diverse, high-performing teams.
Diverse Lynx logo

About Diverse Lynx

Sourced by ZipRecruiter

Diverse Lynx, based in Princeton, NJ, US, is a reputable company in the Information Technology sector. The firm, as reflected through its website diverselynx.com, specializes in delivering comprehensive IT solutions. These solutions range from IT consulting to robust digital transformation strategies, IT staffing, and full-time placements services. The company was established in 2008, and it prides itself on providing simplified, efficient technology solutions designed to meet the unique needs of each client.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

Princeton, NJ, US

Year founded

2002

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