Assistant Research Engineer - Computer Vision VectorCam Project Faculty

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 Vector Cam platform. Vector Cam 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)

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.

Computer Vision and Model Development:
Design and train deep learning models for insect classification and morphological recognition. Experiment with architectures such as Efficient Net, 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 Tensor Flow 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,…