Computer hardware engineer Jobs in Baltimore, MD

Job Description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a Senior Computer Vision Engineer to own applied model development across computer vision, machine learning, and data science use cases — building and deploying solutions for object detection, image segmentation, classification, and video analysis. You will evaluate and fine-tune model architectures using PyTorch and TensorFlow, build broader ML models for forecasting and anomaly detection, and apply practical knowledge of computer vision hardware including cameras, sensors, and edge devices.

MUST HAVES

- You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;

- 3 to 5 years of professional experience in Computer Vision, Machine Learning, Data Science, or a related field;

- Degree in Computer Science, Engineering, Data Science, Mathematics, or a related discipline (or equivalent practical experience);

- Engineers located in the US must reside in Dallas, TX, and be open to working from the office (onsite);

- Strong, production-level proficiency in Python;

- Deep hands-on experience with PyTorch and/or TensorFlow;

- Proven track record of building and deploying models for detection, segmentation, classification, and image/video analysis;

- Solid understanding of broader ML and data science techniques (time-series modeling, forecasting, anomaly detection, regression, and clustering);

- Practical experience working with computer vision hardware, including cameras, sensors, and lighting setups;

- Familiarity with deploying models on edge devices;

- Strong understanding of how physical and real-world constraints impact data quality, model training, and inference;

- Upper-intermediate English level.

WHAT YOU WILL DO

- Own the applied model development process across computer vision, AI/ML, and broader data science use cases;

- Translate complex business problems into viable, practical, and scalable AI/ML solutions;

- Evaluate various model options, train and fine-tune selected architectures, and rigorously analyze model performance;

- Develop and deploy solutions for object detection, image segmentation, image classification, and video analysis;

- Build and maintain models for time-series forecasting, anomaly detection, regression, clustering, and general data analysis;

- Apply practical knowledge of real-world constraints—such as lighting, sensor limitations, and edge device compute power—to ensure optimal data quality and robust model performance in production.

PERKS AND BENEFITS

- Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.

- Competitive compensation: USD-based pay with education, fitness, and team activity budgets.

- Exciting projects: Modern solutions with Fortune 500 and top product companies.

- Flextime: Flexible schedule with remote and office options.

Requirements
3 to 5 years of professional experience in Computer Vision, Machine Learning, Data Science, or a\ related field. Degree in Computer Science, Engineering, Data Science, Mathematics, or a related discipline (or equivalent practical experience). Engineers located in the US must reside in Dallas, TX, and be willing to work onsite. Programming: Strong, production-level proficiency in Python. Frameworks: Deep hands-on experience with leading deep learning frameworks, specifically PyTorch and/or TensorFlow. Computer Vision: Proven track record of building and deploying models for detection, segmentation, classification, and image/video analysis. Machine Learning: Solid understanding of broader ML and data science techniques (time-series modeling, forecasting, anomaly detection, regression, and clustering).