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

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Deep Learning Quantization information

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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
Architect - Gen AI ML - New York / Jersey City

Architect - Gen AI ML - New York / Jersey City

Photon

Irving, TX

Other

Medical, Dental, Vision, Retirement, PTO

Posted 3 days ago

Job description

Role Summary:
We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.

Technical Stack:

LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.

Frameworks: LangChain, LlamaIndex, and Hugging Face.

Orchestration: LangGraph and Multi-Agent Systems (MAS).

Development: Python, FastAPI, and Asynchronous Programming.

RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.

ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.

Deployment: Docker, Production API management, and LLM monitoring.

Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.

Key Responsibilities:

Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.

Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.

Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.

Design and deploy high-performance, scalable backend services using FastAPI and Async Python.

Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.

Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.

Containerize and deploy AI services via Docker to production environments.

Required Qualifications:

Hands-on experience building and deploying GenAI applications in a production setting.

Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).

Experience with vector search, embedding models, and advanced data retrieval patterns.

Knowledge of model fine-tuning techniques and local LLM quantization/hosting.

Familiarity with production-grade monitoring, API security, and CI/CD for ML.

Compensation, Benefits and Duration

Minimum Compensation: USD 79,000
Maximum Compensation: USD 276,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

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