Assistant Research Engineer - Computer Vision The VectorCam Project

Johns Hopkins University; Baltimore (MD); Sur place; USD 65 000 - 85 000; Plein temps

Résumé du poste

A reputable research institution in Baltimore is seeking an Assistant Research Engineer to lead computer vision and AI development for the VectorCam platform. This role involves designing and evaluating machine learning models, managing datasets, and optimizing performance for mobile and cloud applications. Applicants should have a Master’s degree in Computer Science or a related field, strong programming skills in Python, and a solid background in computer vision and deep learning. The position offers a competitive salary and supports global health initiatives.

Qualifications

• Master's degree required.
• Significant experience with computer vision and AI.
• Experience deploying models on edge devices required.

Responsabilités

• Lead development and evaluation of computer vision models.
• Maintain training pipeline and dataset management.
• Develop strategies for mobile device model deployment.

Connaissances

Computer vision

Machine learning

Deep learning

Data management

Python programming

Formation

Master’s degree in Computer Science or related field

Outils

PyTorch

TensorFlow

Weights & Biases

OpenCV

HuggingFace

Description du poste

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 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.

Location & Duration

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

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.

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.

Equal Opportunity Employer

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristic. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors that are irrelevant to the program involved.

Background Checks

The successful candidate(s) for this position will be subject to a pre-employment background check including education verification.

EEO is the Law

https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf