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Deep Learning Quantization Jobs in Severn, MD (NOW HIRING)

Cloudera

AI Solutions Architect

Washington, DC · Remote

Optimize model inference for production environments using quantization, pruning, and hardware ... Expertise in Python and deep learning frameworks (PyTorch, TensorFlow, Hugging Face). * Hands-on ...

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 · 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 · 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 · 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 ...

All JobsDeep Learning Quantization JobsDeep Learning Quantization Jobs in Severn, MD

Deep Learning Quantization information

See Severn, MD salary details

$12.2K

$93.3K

$155.6K

How much do deep learning quantization jobs pay per year?

As of Jun 27, 2026, the average yearly pay for deep learning quantization in Severn, MD is $93,254.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,000.00 and $154,500.00 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 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 job categories do people searching Deep Learning Quantization jobs in Severn, MD look for? The top searched job categories for Deep Learning Quantization jobs in Severn, MD are:
What cities near Severn, MD are hiring for Deep Learning Quantization jobs? Cities near Severn, MD with the most Deep Learning Quantization job openings:
Deep Learning Quantization jobs near you

Machine Learning Engineer, Detection and Tracking

Helsing

Washington, DC • On-site

Full-time

Posted 9 days ago

Job description

Job Summary:
Helsing develops artificial intelligence-enabled capabilities to protect and defend democracies. The Machine Learning Engineer will own the detection and tracking models powering Helsing's products, managing the full model lifecycle from data assessment to deployment on edge platforms.
Responsibilities:
• Training and fine-tuning detection models (YOLO, DETR, Faster R-CNN, and similar architectures) on mission-specific datasets
• Implementing and improving multi-object tracking pipelines (SORT, DeepSORT, ByteTrack, or similar)
• Evaluating model performance: analyzing metrics, diagnosing failure modes, and iterating on data and model improvements
• Managing the data pipeline end-to-end: assessing raw data, coordinating annotation, curating datasets, and implementing augmentation strategies
• Optimizing models for deployment on SWaP-constrained and embedded platforms (quantization, pruning, TensorRT, ONNX export)
• Collaborating with systems engineers to integrate models into the broader Altra platform
• Working across sensor modalities as needed, including electro-optical, infrared, and other imaging sources
Qualifications:
Required:
• Have 5+ years of experience in applied machine learning or computer vision
• Have a Bachelor's degree in Computer Science, Electrical Engineering, or a related field; Master's or PhD strongly preferred
• Have production experience training and deploying object detection models — not just research or academic projects
• Are proficient in Python and PyTorch or a comparable deep learning framework
• Have strong intuition for data quality; you can look at annotated datasets, training curves, and evaluation metrics and know what's wrong
• Have experience with the full model training lifecycle: data curation, annotation management, training, evaluation, and deployment
• Have experience optimizing models for deployment on SWaP-constrained and edge platforms (TensorRT, ONNX, quantization)
• Understand multi-object tracking and have implemented or worked with tracking algorithms in practice
• Can read and contextualize scientific papers in computer vision and apply findings to production systems
• Are a U.S. citizen with an active security clearance or the ability to obtain one
Preferred:
• Strong proficiency in Rust or C++ for production model deployment and optimization
• Experience with multiple sensor modalities — particularly infrared or thermal imaging
• Familiarity with MLOps tooling: experiment tracking (MLflow, Weights & Biases), dataset versioning, model registries
• Experience with annotation tools and workflows (CVAT, Label Studio, or similar)
• Background in computer vision beyond detection — segmentation, pose estimation, activity recognition
• Experience with simulators, emulators, or synthetic data generation for training and evaluation
• Experience deploying models on GPU-accelerated embedded platforms (NVIDIA Jetson, similar)
• Background in defense, intelligence, or other mission-critical environments
Company:
Helsing develops AI-powered defense tech, focusing on drones and software, to enhance military capabilities for democratic nations. Founded in 2021, the company is headquartered in Munich, DEU, with a team of 501-1000 employees. The company is currently Late Stage.

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