Lead Machine Learning Engineer Computer Vision

About the job Lead Machine Learning Engineer Computer Vision 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.

• 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, Tensor Flow, 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.

• 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
  .