Assistant Research Engineer - Computer Vision VectorCam Project

Overview

Salary: $65,000 - $85,000 per year

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.

Baltimore, MD, USA (in-person job) 40 hours a week.

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.

• 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 Tensor Flow.
• 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.

• Experience with model deployment on Android devices or mobile platforms.
• Experience with…