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Deep Learning Quantization Jobs in Texas (NOW HIRING)

Senior Deep Learning Engineer

Austin, TX · On-site +1

$130K - $180K/yr

We're hiring 3 Senior Deep Learning Engineers to join our Neural Networks team. Your primary focus ... Familiarity with model compression techniques like quantization, pruning, etc. These are permanent ...

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

Conduct research on model efficiency, quantization, compression, and on-device deployment ... Stay current on leading research in deep learning, generative AI, and distributed ML * Analyze ...

ML Features Solutions Engineer

Austin, TX · On-site

$81K - $109K/yr

... and deep learning frameworks: PyTorch (required), TensorFlow, or JAX • Experience with model optimization techniques: quantization, pruning, distillation, efficient inference • Strong ...

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

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

AI Algorithms/Software Engineer

Austin, TX · On-site

$96K - $132K/yr

Develops advanced quantization-aware and analog-aware retraining algorithms leveraging PyTorch and ... Stay current with advances in deep learning research and neural network frameworks. Here's the ...

... quantization techniques and model optimization • Experience with high-performance computing and low-latency system design • Familiarity with deep learning frameworks and neural network ...

AI Algorithms/Software Engineer

Austin, TX · On-site

$96K - $132K/yr

Develops advanced quantization-aware and analog-aware retraining algorithms leveraging PyTorch and ... Stay current with advances in deep learning research and neural network frameworks. Here's the ...

AI Algorithms/Software Engineer

Austin, TX · On-site

$96K - $132K/yr

Develops advanced quantization-aware and analog-aware retraining algorithms leveraging PyTorch and ... Stay current with advances in deep learning research and neural network frameworks. Here's the ...

Senior Machine Learning Engineer

Austin, TX

$103K - $142K/yr

Implement model optimization techniques such as quantization, pruning, distillation, and hardware ... Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers. * Proven experience ...

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Deep Learning Quantization information

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.
What cities in Texas are hiring for Deep Learning Quantization jobs? Cities in Texas with the most Deep Learning Quantization job openings:
Senior Deep Learning Engineer

Senior Deep Learning Engineer

Targeted Talent

Austin, TX • On-site, Remote

$130K - $180K/yr

Full-time

Posted 9 days ago

Be an early applicant


Job description

We're seeking top-notch engineers to join our team. As part of our group, you'll collaborate with hardware and software engineers to design, develop, and optimize software for our chip, making AI inference accessible to everyone. You'll excel in identifying and resolving functional/performance bottlenecks in complex software and hardware designs.

We're hiring 3 Senior Deep Learning Engineers to join our Neural Networks team. Your primary focus will be optimizing neural networks to efficiently run on our hardware and building a model optimization pipeline. If you thrive on pushing the boundaries of AI technology, this role is for you!

Requirements:

  • Bachelor's degree in Computer Science, Engineering, or related field
  • 5+ years of experience, with at least 2 years in both deep learning and software engineering
  • Proficiency in deep learning frameworks like Tensorflow and/or PyTorch
  • Experience with CNNs, LSTMs/RNNs, Transformers
  • Strong math skills and Python proficiency
  • Experience with C/C++

Preferred Skills & Experience:

  • Master's or PhD in Computer Science, Engineering, or related field
  • Experience in embedded or low-level programming
  • Knowledge of CUDA/OpenGL
  • Experience deploying neural networks in production
  • Familiarity with model compression techniques like quantization, pruning, etc.
These are permanent full time remote positions.

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Industry

Recruiting and staffing services

Company size

11 - 50 Employees

Headquarters location

Vancouver, BC, CA