Design and train deep learning models for insect classification and morphological recognition ... Investigate quantization, pruning, and other model optimization techniques to ensure efficient ...
Design and train deep learning models for insect classification and morphological recognition ... Investigate quantization, pruning, and other model optimization techniques to ensure efficient ...
URGENT NEED - AI/ML Subject Matter Expert (SME) ___________Baltimore, MD - ONSITE
Baltimore, MD · On-site
Cost Awareness: * Demonstrate cost control strategies such as model quantization, optimized ... Strong background in AI/ML and deep understanding of machine learning algorithms and techniques.
URGENT NEED - AI/ML Subject Matter Expert (SME) ___________Baltimore, MD - ONSITE
Baltimore, MD · On-site
Cost Awareness: * Demonstrate cost control strategies such as model quantization, optimized ... Strong background in AI/ML and deep understanding of machine learning algorithms and techniques.
Data Scientist Level 4 with Security Clearance
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 ...
Data Scientist Level 4 with Security Clearance
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 ...
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 ...
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 ...
Data Scientist Level 4
$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 ...
Data Scientist Level 4
$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 ...
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 ...
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 ...
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?
| 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 Maryland? For Deep Learning Quantization jobs in Maryland, the most frequently searched job titles are:
What job categories do people searching Deep Learning Quantization jobs in Maryland look for? The top searched job categories for Deep Learning Quantization jobs in Maryland are:
What cities in Maryland are hiring for Deep Learning Quantization jobs? Cities in Maryland with the most Deep Learning Quantization job openings:
Assistant Research Engineer - Computer Vision The VectorCam Project
Baltimore, MD • On-site
Full-time
Posted 9 days ago
Johns Hopkins Medicine rating
7.5
Based on 202 frontline employees who took The Breakroom Quiz
226th of 875 rated healthcare providers
Job description
Description
The Johns Hopkins Center for Bioengineering Innovation & Design (CBID) in the Department of Biomedical Engineering is seeking an Assistant Research Engineer to lead computer vision and AI development for the VectorCam platform. VectorCam is an AI-enabled mobile imaging system designed to allow community health workers to identify mosquito species in real time, enabling faster vector surveillance and improved malaria control strategies. This role will serve as the technical lead for computer vision and image analysis within the project, responsible for designing and iterating on machine learning architectures, managing training pipelines and datasets, and optimizing models for deployment across edge and cloud environments. The successful candidate will work at the intersection of computer vision, edge AI deployment, mobile imaging systems, and global health field implementation. The role requires someone who is highly experimental and curious, constantly exploring new model architectures and approaches while pushing the performance and reliability of the AI system. The ideal candidate will also demonstrate strong attention to detail in data management and data science practices, and be able to clearly articulate the probability, statistics, and evaluation methods used when defending model design choices and performance claims.
Department: Johns Hopkins Center for Bioengineering Innovation & Design (CBID), Department of Biomedical Engineering, Whiting School of Engineering
Location & Duration: Baltimore, MD, USA (in-person job)
Reports to: Dr. Soumyadipta Acharya (Principal Investigator)
Key Responsibilities
Lead the design, training, and evaluation of computer vision models for mosquito identification and other relevant projects in vector-borne diseases. Develop and maintain a scalable training and evaluation pipeline for image classification and detection models. Continuously explore and evaluate new architectures, training approaches, and optimization strategies to improve model accuracy and robustness. Design and maintain systems for dataset management, ensuring training, validation, and test datasets remain clean, versioned, and traceable. Maintain high standards of data organization and reproducibility across experiments and training pipelines. Develop strategies for deploying models across mobile edge devices and cloud infrastructure. Optimize models for inference on smartphones and other resource-constrained platforms. Work closely with software engineers to integrate models into the Android application and imaging pipeline. Investigate and troubleshoot performance issues related to camera systems, imaging conditions, and device variability. Develop benchmarking and evaluation methods to continuously monitor model performance across deployments. Apply statistical reasoning when evaluating model performance and clearly communicate the statistical basis for model improvements and algorithmic decisions. Collaborate with entomologists and field teams to improve data collection, labeling, and training dataset quality. Contribute to publications and presentations describing algorithm development and system performance.
Technical Focus Areas
Computer Vision and Model Development: Design and train deep learning models for insect classification and morphological recognition. Experiment with architectures such as EfficientNet, YOLO, Vision Transformers, and other modern computer vision models to determine optimal approaches for the application. Develop strategies for handling limited datasets, noisy data, and challenging real-world image conditions.
Model Optimization for Edge Deployment: Optimize models for deployment on smartphones using frameworks such as TensorFlow Lite, PyTorch Mobile, or ONNX. Investigate quantization, pruning, and other model optimization techniques to ensure efficient inference on resource-constrained devices. Ensure models perform consistently across different smartphone cameras and hardware configurations.
AI Data Pipeline and Dataset Management: Develop systems for dataset versioning, experiment tracking, and model reproducibility. Ensure that training, validation, and testing datasets are well organized, auditable, and traceable. Maintain clear documentation of dataset lineage and experiment configurations. Build workflows that support continuous model retraining as new field data becomes available.
System Architecture for AI Deployment: Design the architecture for managing model updates, versioning, and deployment across edge devices and cloud platforms. Develop strategies for monitoring model performance and maintaining reliability across large-scale field deployments.
Project Impact
VectorCam aims to transform how mosquito surveillance is conducted in malaria-endemic regions by enabling rapid and accurate species identification directly in the field. By improving the speed and accessibility of entomological surveillance, this technology has the potential to strengthen malaria control programs and support more targeted vector control interventions. This role offers the opportunity to work on a globally impactful technology while solving challenging problems at the intersection of computer vision, edge AI, and public health innovation.
Qualifications
Qualifications
Master's degree in Computer Science, Machine Learning, Computer Vision, Software Engineering, or a related field. Strong background in computer vision and deep learning. Experience training and evaluating computer vision models using frameworks such as PyTorch or TensorFlow. Strong understanding of probability, statistics, and model evaluation methods, with the ability to clearly explain the reasoning behind model choices and performance metrics. Experience working with image datasets, data pipelines, and model evaluation methodologies. Experience deploying machine learning models to edge devices or mobile platforms. Strong programming skills in Python and experience with machine learning development environments. Strong attention to detail in data management, experiment tracking, and dataset organization. Ability to independently explore technical approaches and rapidly prototype solutions. Interest in applying AI systems to real-world global health challenges.
Preferred Experience
Experience with model deployment on Android devices or mobile platforms. Experience with experiment tracking tools such as Weights & Biases, OpenCV, HuggingFace, Google's ML Kit or similar systems. Experience working with image datasets collected in real-world environments. Experience with edge AI optimization techniques such as quantization or pruning. Experience contributing to applied machine learning research or technical publications.
Application Instructions
Click on the link and apply today!
The Johns Hopkins Center for Bioengineering Innovation & Design (CBID) in the Department of Biomedical Engineering is seeking an Assistant Research Engineer to lead computer vision and AI development for the VectorCam platform. VectorCam is an AI-enabled mobile imaging system designed to allow community health workers to identify mosquito species in real time, enabling faster vector surveillance and improved malaria control strategies. This role will serve as the technical lead for computer vision and image analysis within the project, responsible for designing and iterating on machine learning architectures, managing training pipelines and datasets, and optimizing models for deployment across edge and cloud environments. The successful candidate will work at the intersection of computer vision, edge AI deployment, mobile imaging systems, and global health field implementation. The role requires someone who is highly experimental and curious, constantly exploring new model architectures and approaches while pushing the performance and reliability of the AI system. The ideal candidate will also demonstrate strong attention to detail in data management and data science practices, and be able to clearly articulate the probability, statistics, and evaluation methods used when defending model design choices and performance claims.
Department: Johns Hopkins Center for Bioengineering Innovation & Design (CBID), Department of Biomedical Engineering, Whiting School of Engineering
Location & Duration: Baltimore, MD, USA (in-person job)
Reports to: Dr. Soumyadipta Acharya (Principal Investigator)
Key Responsibilities
Lead the design, training, and evaluation of computer vision models for mosquito identification and other relevant projects in vector-borne diseases. Develop and maintain a scalable training and evaluation pipeline for image classification and detection models. Continuously explore and evaluate new architectures, training approaches, and optimization strategies to improve model accuracy and robustness. Design and maintain systems for dataset management, ensuring training, validation, and test datasets remain clean, versioned, and traceable. Maintain high standards of data organization and reproducibility across experiments and training pipelines. Develop strategies for deploying models across mobile edge devices and cloud infrastructure. Optimize models for inference on smartphones and other resource-constrained platforms. Work closely with software engineers to integrate models into the Android application and imaging pipeline. Investigate and troubleshoot performance issues related to camera systems, imaging conditions, and device variability. Develop benchmarking and evaluation methods to continuously monitor model performance across deployments. Apply statistical reasoning when evaluating model performance and clearly communicate the statistical basis for model improvements and algorithmic decisions. Collaborate with entomologists and field teams to improve data collection, labeling, and training dataset quality. Contribute to publications and presentations describing algorithm development and system performance.
Technical Focus Areas
Computer Vision and Model Development: Design and train deep learning models for insect classification and morphological recognition. Experiment with architectures such as EfficientNet, YOLO, Vision Transformers, and other modern computer vision models to determine optimal approaches for the application. Develop strategies for handling limited datasets, noisy data, and challenging real-world image conditions.
Model Optimization for Edge Deployment: Optimize models for deployment on smartphones using frameworks such as TensorFlow Lite, PyTorch Mobile, or ONNX. Investigate quantization, pruning, and other model optimization techniques to ensure efficient inference on resource-constrained devices. Ensure models perform consistently across different smartphone cameras and hardware configurations.
AI Data Pipeline and Dataset Management: Develop systems for dataset versioning, experiment tracking, and model reproducibility. Ensure that training, validation, and testing datasets are well organized, auditable, and traceable. Maintain clear documentation of dataset lineage and experiment configurations. Build workflows that support continuous model retraining as new field data becomes available.
System Architecture for AI Deployment: Design the architecture for managing model updates, versioning, and deployment across edge devices and cloud platforms. Develop strategies for monitoring model performance and maintaining reliability across large-scale field deployments.
Project Impact
VectorCam aims to transform how mosquito surveillance is conducted in malaria-endemic regions by enabling rapid and accurate species identification directly in the field. By improving the speed and accessibility of entomological surveillance, this technology has the potential to strengthen malaria control programs and support more targeted vector control interventions. This role offers the opportunity to work on a globally impactful technology while solving challenging problems at the intersection of computer vision, edge AI, and public health innovation.
Qualifications
Qualifications
Master's degree in Computer Science, Machine Learning, Computer Vision, Software Engineering, or a related field. Strong background in computer vision and deep learning. Experience training and evaluating computer vision models using frameworks such as PyTorch or TensorFlow. Strong understanding of probability, statistics, and model evaluation methods, with the ability to clearly explain the reasoning behind model choices and performance metrics. Experience working with image datasets, data pipelines, and model evaluation methodologies. Experience deploying machine learning models to edge devices or mobile platforms. Strong programming skills in Python and experience with machine learning development environments. Strong attention to detail in data management, experiment tracking, and dataset organization. Ability to independently explore technical approaches and rapidly prototype solutions. Interest in applying AI systems to real-world global health challenges.
Preferred Experience
Experience with model deployment on Android devices or mobile platforms. Experience with experiment tracking tools such as Weights & Biases, OpenCV, HuggingFace, Google's ML Kit or similar systems. Experience working with image datasets collected in real-world environments. Experience with edge AI optimization techniques such as quantization or pruning. Experience contributing to applied machine learning research or technical publications.
Application Instructions
Click on the link and apply today!
What Johns Hopkins Medicine employees say
Pay
Benefits
Hours and flexibility
Workplace
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About Johns Hopkins Hospital
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Industry
Health care and social assistance
Company size
10,000+ Employees
Headquarters location
Baltimore, MD, US
Year founded
1889