Participate in the development, training, and optimization of state-of-the-art deep learning models ... Hands-on experience with model deployment, quantization, distillation, or inference acceleration ...
Participate in the development, training, and optimization of state-of-the-art deep learning models ... Hands-on experience with model deployment, quantization, distillation, or inference acceleration ...
Participate in the development, training, and optimization of state-of-the-art deep learning models ... Hands-on experience with model deployment, quantization, distillation, or inference acceleration ...
Participate in the development, training, and optimization of state-of-the-art deep learning models ... Hands-on experience with model deployment, quantization, distillation, or inference acceleration ...
AI Engineer
Houston, TX · On-site
$50K - $112K/yr
... networks and deep learning methods for advanced AI applications - Managing data quality and ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...
AI Engineer
Houston, TX · On-site
$50K - $112K/yr
... networks and deep learning methods for advanced AI applications - Managing data quality and ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...
Deep Learning Quantization information
See Pearland, TX salary details
$19.5K is the 25th percentile. Wages below this are outliers.
$9.8K - $20.3K
27% of jobs
$20.3K - $30.7K
0% of jobs
$30.7K - $41.2K
0% of jobs
$41.2K - $51.6K
0% of jobs
$51.6K - $62.1K
0% of jobs
The median wage is $71.7K / yr.
$62.1K - $72.5K
25% of jobs
$72.5K - $83K
18% of jobs
$90.5K is the 75th percentile. Wages above this are outliers.
$83K - $93.5K
7% of jobs
$93.5K - $103.9K
2% of jobs
$103.9K - $114.4K
0% of jobs
$114.4K - $124.8K
21% of jobs
$9.8K
$74.8K
$124.8K
How much do deep learning quantization jobs pay per year?
As of Aug 6, 2026, the average yearly pay for deep learning quantization in Pearland, TX is $74,793.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,200.00 and $123,900.00 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?
| Aspect | Deep Learning Quantization | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Advanced degrees in AI, Computer Science, or related fields; knowledge of neural networks | Bachelor's or Master's in CS, Data Science, or related fields; programming skills |
| Work Environment | Research labs, AI development teams, hardware optimization settings | Software development teams, data-driven projects, product-focused environments |
| Industry Usage | AI hardware optimization, model deployment, edge computing | Model 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.
What are popular job titles related to Deep Learning Quantization jobs in Pearland, TX? For Deep Learning Quantization jobs in Pearland, TX, the most frequently searched job titles are:
What cities near Pearland, TX are hiring for Deep Learning Quantization jobs? Cities near Pearland, TX with the most Deep Learning Quantization job openings:
Other
Posted 9 days ago
Job description
Company Introduction
At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.
Key Responsibilities- Model Implementation & Iteration: Participate in the development, training, and optimization of state-of-the-art deep learning models for autonomous driving, with a focus on end-to-end architectures, including perception, online mapping, and end-to-end planning.
- Full Lifecycle Execution: Engage in the entire machine learning workflow under the guidance of domain experts, spanning from data curation and data analysis to model experimentation, hyperparameter tuning, and rigorous performance metric verification.
- Cross-Functional Collaboration: Partner with simulation, infrastructure, and downstream planning/control teams to deploy, evaluate, and integrate machine learning components into our production pipeline for autonomous trucks.
- Literature Tracking: Stay abreast of the latest research breakthroughs in computer vision and generative AI, and actively bench-test promising SOTA methods to solve real-world corner cases.
This matters as much to us as what you'll ship.
- You get a real mentor. Every engineer is paired with senior-level engineers developing you. Mentorship here is weighted toward design and judgment: how to frame a problem, what to build and why, how to tell whether a solution is actually right.
- We promote fast. Managers are expected to push engineers to attempt work above their current level, and to promote in the next cycle when they deliver it.
- Education: A Bachelor's, Master's, or Ph.D. (including upcoming graduates) in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
- You have trained neural networks. Coursework, research, personal projects, open-source work, and internships all count. We care that you have actually run the loop: built a model, trained it, found out why it was not working, and fixed it.
- Core Knowledge: Strong theoretical foundation in machine learning and deep learning, with a solid understanding of modern architectures (e.g., Transformers, CNNs, Graphs).
- Technical Stack: Proficiency in Python and deep learning frameworks such as PyTorch, along with strong software engineering fundamentals (data structures, algorithms, and clean coding practices).
- Attributes: High self-motivation, strong analytical and problem-solving skills, a fast learner in a high-velocity startup environment, and a strong team-player mindset.
- Computer vision. Research or projects in computer vision, and particularly in 3D.
- Specific Research Directions: Academic thesis or deeply focused research experience in one or more of the following domains:
- Computer Vision (2D or 3D)
- Online Mapping, Vectorization, or Visual SLAM
- Prediction and Behavioral Modeling
- Academic Achievements: A track record of research publications in machine learning, computer vision, or robotics conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICRA, IROS).
- Engineering Plus: Hands-on experience with model deployment, quantization, distillation, or inference acceleration tools (e.g., TensorRT, ONNX, CUDA, C++).
- Industry Exposure: Prior internship experience within the autonomous driving industry or advanced robotics labs.