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Deep Learning Quantization Jobs in Kansas (NOW HIRING)

EnCharge AI

$175K - $245K/yr

Research and develop quantization-aware training (QAT) and post-training quantization (PTQ) techniques for deep learning models. * Implement low-bit precision optimizations (e.g., INT8, BF16)

Waabi

$195K - $286K/yr

... quantization). - Publications at top-tier ML/CV venues (NeurIPS, ICML, CVPR, ICLR, ECCV) in model compression, efficient deep learning, or related areas. - Experience distilling large generative ...

PICTOR LABS INC

$89K - $123K/yr

The ideal candidate will have deep expertise in ML inference optimization, GPU programming, and ... Prototype and productionize new inference optimization techniques, including quantization, pruning ...

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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.
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What job categories do people searching Deep Learning Quantization jobs in Kansas look for? The top searched job categories for Deep Learning Quantization jobs in Kansas are:
Deep Learning Quantization jobs near you

AI Research Engineer

EnCharge AI

$175K - $245K/yr

Other

Posted 10 days ago

Job description

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge's robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today's best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

About the Role

EnCharge AI is looking for an experienced AI Research Engineer to optimize deep learning models for deployment on edge AI platforms. You will work on model compression, quantization strategies, and efficient inference techniques to improve the performance of AI workloads. 

Responsibilities

  • Research and develop quantization-aware training (QAT) and post-training quantization (PTQ) techniques for deep learning models.

  • Implement low-bit precision optimizations (e.g., INT8, BF16).

  • Design and optimize efficient inference algorithms for AI workloads, focusing on latency, memory footprint, and power efficiency.

  • Work with frameworks such as PyTorch, ONNX Runtime, and TVM to deploy optimized models.

  • Analyze accuracy trade-offs and develop calibration techniques to mitigate precision loss in quantized models.

  • Collaborate with hardware engineers to optimize model execution for edge devices, and NPUs.

  • Contribute to research on knowledge distillation, sparsity, pruning, and model compression techniques.

  • Benchmark performance across different hardware and software stacks.

  • Stay updated with the latest advancements in AI efficiency, model compression, and hardware acceleration. 

Qualifications

  • Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field.

  • Strong expertise in deep learning, model optimization, and numerical precision analysis.

  • Hands-on experience with model quantization techniques (QAT, PTQ, mixed precision).

  • Proficiency in Python, C++, CUDA, or OpenCL for performance optimization.

  • Experience with AI frameworks: PyTorch, TensorFlow, ONNX Runtime, TVM, TensorRT, or OpenVINO.

  • Understanding of low-level hardware acceleration (e.g., SIMD, AVX, Tensor Cores, VNNI).

  • Familiarity with compiler optimizations for ML workloads (e.g., XLA, MLIR, LLVM). 

EnchargeAI is an equal employment opportunity employer in the United States.

The salary range for this position is $180,000 to $240,000 USD per year. (Per Year: $175,000 to $245,000 CAD | 116,000 to 154,000 EUR)
Actual compensation offered will be determined based on job-related knowledge, skills, and experience.

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