Job Title: Computer Vision/Deep Learning Scientist

Location: Atlanta, GA 30308 (Hybrid: 3x/Week onsite)

Contract duration: 6+ Months, Long-term.

Levels: Junior, Intermediate, Senior

Industry: Rail Transportation.

Role Overview

We are seeking highly motivated Computer Vision / Deep Learning Scientists to join our team and work across multiple product teams and business units. In this role, you will identify high-impact business problems and design, develop, and deploy advanced computer vision and deep learning solutions from concept through production. You will collaborate closely with cross-functional partners, conduct applied research, and continuously evaluate and improve model performance throughout the solution lifecycle.

Key Responsibilities

Partner with product, engineering, and business stakeholders to define and solve real-world problems using computer vision and deep learning techniques
Design, develop, and implement novel computer vision and deep learning models for unique and challenging use cases
Build, train, evaluate, and optimize models for tasks such as image classification, object detection, and semantic segmentation
Assess model accuracy, robustness, and data quality; iterate based on performance metrics and business requirements
Conduct research to stay current with emerging algorithms, architectures, and industry best practices
Leverage GPU-accelerated environments and distributed GPU clusters to train and evaluate models at scale
Contribute to a highly collaborative, research-driven engineering culture

Required Technical Skills

Strong proficiency in Python
Hands-on experience with deep learning frameworks such as TensorFlow, Keras, and/or PyTorch
Experience with computer vision and image processing using deep learning techniques, including:
Object detection
Image classification
Semantic segmentation
Familiarity with common CNN architectures such as VGG, ResNet, MobileNet, and related variants
Experience with traditional computer vision and image processing techniques using tools such as OpenCV and scikit-image (skimage)
Experience working in GPU-based computing environments; exposure to distributed or parallel training is a plus

Qualifications

Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Statistics, or a related field
Industry or research experience commensurate with level (Junior, Intermediate, Senior)

Nice to Have (Level-Dependent)

Experience deploying models to production environments
Familiarity with MLOps practices and model monitoring
Publications, patents, or open-source contributions in computer vision or deep learning

Computer Vision/Deep Learning Scientist (Junior–Senior) role in the rail transportation industry focused on identifying high-impact business problems and designing, developing, training, evaluating, optimizing, and deploying computer vision and deep learning solutions from concept through production. Responsibilities include collaborating with product, engineering, and business stakeholders; building models for image classification, object detection, and semantic segmentation; assessing model accuracy, robustness, and data quality against business requirements; conducting applied research on emerging algorithms and architectures; and training models at scale using GPU-accelerated and distributed computing environments. Required qualifications include strong Python proficiency; hands-on experience with TensorFlow, Keras, and/or PyTorch; experience with CNN architectures such as VGG, ResNet, and MobileNet; practical use of traditional computer vision tools such as OpenCV and scikit-image; experience working in GPU-based environments; and a Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Statistics, or a related field, with industry or research experience appropriate to level. Nice-to-have qualifications (level-dependent) include production model deployment, MLOps and model monitoring experience, and publications, patents, or open-source contributions in computer vision or deep learning.

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