What are the responsibilities and job description for the Computer Vision/Deep Learning Scientist position at Open Systems Inc.?
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.