The posting
Responsibilities
- Lead the design and development of end-to-end computer vision systems, from data acquisition and preprocessing to model deployment and monitoring.
- Architect scalable ML pipelines for vision domain including detection, segmentation, and multimodal learning, ensuring reliability in both batch and real-time environments.
- Drive research and adoption of state-of-the-art CV and ML techniques (e.g., Transformers, Vision-Language Models, diffusion models) to solve complex image understanding problems.
- Oversee the collection, annotation, and augmentation of training datasets, ensuring diversity and quality for robust model performance.
- Build frameworks for model experimentation, reproducibility, and lifecycle management (MLflow, Weights & Biases, or similar).
- Collaborate with data engineers to integrate ML workflows into scalable data pipelines and ensure smooth deployment on cloud platforms (AWS, Azure, GCP).
- Partner with product and business teams to translate business needs into ML solutions, balancing research depth with delivery timelines.
- Establish and enforce best practices for ML code quality, testing, version control, and CI/CD pipelines.
- Mentor and lead a team of ML/CV engineers, conducting code reviews, guiding experiments, and fostering a culture of technical excellence.
- Represent the ML team in cross-functional discussions, contributing to long-term AI/ML strategy and roadmap.
Requirements
- Bachelors or master's degree in computer science, Machine Learning, Computer Vision, or related field.
- 5+ years of professional experience in computer vision and machine learning engineering, with at least 2+ years in a technical leadership role.
- Strong expertise in Python and ML/CV frameworks: PyTorch, TensorFlow, OpenCV.
- Proven experience with deep learning architectures (CNNs, RNNs, Transformers, Vision-Language Models).
- Hands-on experience with segmentation, object detection, image similarity, and multimodal approaches.
- Experience in designing scalable ML pipelines with tools such as Apache Airflow, Docker, and Kubernetes.
- Familiarity with cloud-based ML platforms (AWS Sagemaker, GCP Vertex AI, Azure ML).
- Solid background in model monitoring, explainability, and performance optimization.
- Strong leadership and communication skills, with a track record of mentoring engineers and managing cross-team initiatives.
Nice to Have
- Research experience or publications in computer vision, multimodal AI, or generative models.
- Exposure to 3D vision, AR/VR, CAD/BIM, or graphics pipelines.
- Knowledge of synthetic data generation, simulation environments, or reinforcement learning.
- Experience with federated learning, edge AI, or on-device ML optimization.
