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Deep Learning Quantization Jobs in Dallas, 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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$10.9K

$83K

$138.5K

How much do deep learning quantization jobs pay per year?

As of May 29, 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 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.

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 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 are popular job titles related to Deep Learning Quantization jobs in Dallas, TX? For Deep Learning Quantization jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Dallas, TX look for? The top searched job categories for Deep Learning Quantization jobs in Dallas, TX are:
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
Computer Vision Engineer

Computer Vision Engineer

Burnetts Staffing

Fort Worth, TX

$43.27 - $61/hr

Full-time

Medical, Dental, Vision, Retirement

Posted 7 days ago

Job description

Our client, a rapidly growing organization operating in a complex industrial environment, is seeking a Computer Vision Engineer in Fort Worth, TX to design and build a real-time video analytics platform. This system processes streams from dozens of cameras to detect, track, and analyze human movement, converting video data into actionable operational insights.
This is a highly hands-on, end to end role where you will own the perception pipeline from video ingestion through model inference to event delivery. The ideal candidate is a strong builder who thrives in production environments, enjoys solving real-world challenges, and is comfortable making key technical decisions around architecture, model selection, and performance trade offs.
You will work closely with a small, fast-moving team and have direct influence on future system capabilities, including safety monitoring, activity recognition, and anomaly detection.


Required Qualifications :
  • 3+ years of experience developing and deploying computer vision solutions in production
  • Strong experience with multi-object tracking (e.g., DeepSORT, ByteTrack, BoT-SORT, or similar)
  • Proficiency in Python and deep learning frameworks (PyTorch or TensorFlow)
  • Experience designing and optimizing real-time video pipelines (RTSP, multi-stream processing)
  • Familiarity with inference optimization (TensorRT, ONNX, quantization techniques)
  • Ability to work in dynamic environments with evolving requirements
  • Strong communication skills with the ability to document and explain technical decisions

Hours: Monday - Friday 8:00 AM to 5:00 PM

Benefits :
  • Medical
  • Dental
  • Vision
  • 401(k)
Compensation: $43.27-61.00/ Hourly
For immediate consideration, apply now!
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