hoy
Liderar el diseño, desarrollo e implementación de sistemas de visión por computadora de aprendizaje automático de extremo a extremo y pipelines de ML, y mentorear a un equipo de ingenieros de ML/CV.
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.
Publicada el 9 nov 2026
