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Deep Learning Quantization Jobs in Washington, DC

Twenty

Applied AI Engineer

Washington, DC

You have strong Python skills and deep learning experience with PyTorch, TensorFlow, or JAX. * You ... You have implemented quantization or other optimization techniques to improve inference efficiency.

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

You dive deep. It's important for you to really know how things work. You're always building ... Experience with model compression techniques (quantization, pruning, distillation) * Contributions ...

Markon

Data Scientist Level 4

Fort George G Meade, MD

$220K - $235K/yr

Deep understanding of machine learning architectures, model selection, training, and optimization ... Strong background in AI/ML performance optimization, including model compression, quantization, or ...

Markon

Data Scientist Level 4

Fort George G Meade, MD · On-site

$220K - $235K/yr

Deep understanding of machine learning architectures, model selection, training, and optimization ... Strong background in AI/ML performance optimization, including model compression, quantization, or ...

Markon

Data Scientist Level 4

Fort George G Meade, MD

$220K - $235K/yr

Deep understanding of machine learning architectures, model selection, training, and optimization ... Strong background in AI/ML performance optimization, including model compression, quantization, or ...

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All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Washington, DC

Deep Learning Quantization information

See Washington, DC salary details

$12.5K

$95K

$158.6K

How much do deep learning quantization jobs pay per year?

As of Jul 14, 2026, the average yearly pay for deep learning quantization in Washington, DC is $95,008.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,500.00 and $157,400.00 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?

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 are popular job titles related to Deep Learning Quantization jobs in Washington, DC? For Deep Learning Quantization jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Washington, DC look for? The top searched job categories for Deep Learning Quantization jobs in Washington, DC are:
Deep Learning Quantization jobs near you

Senior RF Machine Learning Engineer

Quartermaster AI Inc

Arlington, VA • On-site

$210K - $260K/yr

Full-time

Posted 27 days ago

Job description

About Us:
At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most.
Job Description:
Quartermaster AI is seeking a Senior AI/ML Engineer with an emphasis in RF analysis to develop and deploy machine learning systems that utilize RF data for real-time maritime intelligence.
You'll work in a small team of experienced engineers to build detection, classification, and tagging models that help provide contextual understanding of vessel activity based on observed RF signatures.
Key Responsibilities:
  • Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking.
  • Build and maintain dataset curation pipelines, including AIS-correlated ground truth labeling, synthetic RF data generation, and augmentation strategies for class-imbalanced maritime environments.
  • Build the interface between DSP feature outputs and model inputs by defining pre-processing, normalization, and feature extraction requirements in coordination with the DSP engineer.
  • Develop model evaluation frameworks and benchmarking harnesses; define quantitative performance criteria and drive iterative improvement against them.
  • Optimize models and inference workflows for deployment on edge compute hardware.
  • Document model architecture, training methodology, dataset provenance, and validation results.
Qualifications (Preferred):
  • Master's or PhD in Machine Learning, Signal Processing, or a closely related field - or equivalent demonstrated experience.
  • 5+ years building and deploying ML systems with a focus on RF or signals data.
  • Proficiency in Python and deep learning frameworks; familiarity with RF-native tooling such as Torchsig is a strong plus.
  • Strong understanding of signal alignment, temporal synchronization, and feature extraction from IQ and spectral data.
  • Proven ability to ship production models, not just research prototypes.
  • Experience in maritime, aerospace, or operationally demanding spectral environments.
  • Experience building labeled RF datasets from ground truth sources.
  • Familiarity with edge inference constraints and optimization techniques (quantization, pruning, model distillation).
  • Active Secret clearance or demonstrated ability to obtain one.

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