Computer Vision Engineer

If you thrive in a hybrid work environment and are excited to tackle real-world challenges in computer vision, we want to hear from you! This role in hybrid, onsite 3 days a week in Boston.

Key Responsibilities:

• Design, develop, and deploy scalable and production-ready computer vision models for real-world applications.
• Leverage algorithms and frameworks like YOLO, U-Net, CNNs/RCNNs, and other deep learning architectures for tasks such as image segmentation, object detection, and classification.
• Implement pre- and post-processing techniques, including image blurring, contrast enhancements, and other image augmentation pipelines, to optimize model performance.
• Write efficient, maintainable, and clean Python code for integrating computer vision models into production systems.
• Collaborate with cross-functional teams, including data scientists, machine learning engineers, and product managers, to understand project needs and deliver solutions.
• Analyze and improve model performance, accuracy, and efficiency through continuous evaluation and enhancement.
• Stay updated on the latest advancements in computer vision and machine learning techniques, incorporating new technologies where appropriate.
• Contribute to model optimization and inference processes to ensure scalability and low latency in production environments.
• Troubleshoot production issues and implement solutions to improve reliability and performance.
• Document technical solutions, workflows, and methodologies for internal and external stakeholders.
• 3+ years of professional experience working as a Machine Learning Engineer or in a related role focused on computer vision.
• Strong proficiency in Python and experience with deep learning libraries such as TensorFlow, PyTorch, or Keras.
• Hands-on expertise with computer vision models and architectures, including YOLO, U-Net, and CNNs/RCNNs.
• Extensive experience with image segmentation, object detection, and related computer vision techniques.
• Familiarity with preprocessing methods like image blurring and contrast enhancements to improve model outcomes.
• Proven ability to build and deploy production-ready code for machine learning models at scale.
• Understanding of best practices for scalable system design, deployment, and maintenance of machine learning pipelines.
• Strong problem-solving skills and a passion for tackling complex, unstructured problems in the domain of computer vision.
• Effective verbal and written communication skills to articulate solutions to both technical and non-technical stakeholders.

Preferred Qualifications:

• Master’s Degree or Ph.D. in Computer Science, Data Science, Machine Learning, or a related technical field is preferred.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Exposure to modern DevOps pipelines and CI/CD workflows for machine learning deployment.
• Previous experience working in a hybrid team environment or collaborating with diverse teams.