Computer Vision Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is an opportunity to contribute to technology initiatives in a collaborative environment with opportunities for professional growth. 

Job Title: Computer Vision Engineer 

Location: Remote 

Position Type: Full-time, Direct W2 - [Not open to C2C or 1099] 

Salary Range: $115,000–$165,000 Annually 

Experience Required: 5+ years  

Sponsorship: U.S. Citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply. Bright Vision Technologies is unable to sponsor new H-1B visa petitions for this position.  

Job Summary 

Bright Vision Technologies is seeking a Computer Vision Engineer to develop image and video analysis solutions for enterprise applications. The engineer will prepare visual datasets, build and evaluate computer vision models, optimize inference workflows, and collaborate with software and data teams to integrate reliable visual intelligence into production systems. 

Key Responsibilities 

• Develop computer vision pipelines for image classification, object detection, segmentation, or visual inspection use cases. 

• Prepare, annotate, validate, and augment image or video datasets for model development. 

• Train, fine-tune, and evaluate models using appropriate architectures and task-specific metrics. 

• Optimize model inference for accuracy, throughput, latency, and deployment constraints. 

• Integrate vision models into APIs, batch workflows, or edge/application environments and document model performance. 

Required Qualifications 

• Experience Required: typically 5+ years in computer vision, machine learning, or image-processing engineering; align with the approved requisition. 

• Strong Python programming and experience with OpenCV and image-processing workflows. 

• Hands-on knowledge of PyTorch or TensorFlow for developing or fine-tuning vision models. 

• Understanding of image classification, object detection, segmentation, data augmentation, and evaluation metrics. 

• Experience preparing and analyzing image or video datasets and diagnosing model performance issues. 

• Working knowledge of model deployment, REST APIs, Git, and software testing practices. 

• Strong analytical, debugging, experimentation, and communication skills. 

• Experience working in iterative software delivery teams, participating in planning, defect triage, and release validation as appropriate to the assignment. 

Preferred Qualifications 

• Experience with YOLO, Detectron2, torchvision, or comparable computer vision frameworks. 

• Exposure to GPU inference optimization, ONNX, TensorRT, or edge deployment. 

• Familiarity with cloud ML platforms, data labeling workflows, and model monitoring. 

• Relevant computer vision, machine learning, or cloud certification. 

How to Apply 

Would you like to know more about this opportunity? For immediate consideration, please send your resume to Jenny@bvteck.com or contact Jennifer at (908) 505-3544. Learn more about Bright Vision Technologies at www.bvteck.com. 

Bright Vision Technologies is an Equal Opportunity Employer. 

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.