Machine Learning Engineering Manager

About the role

Hydrosat is building a next‑generation Earth Observation constellation and seeks a Machine Learning Engineering Manager to lead a team of scientists and engineers. You will guide the development of scalable deep‑learning models that turn thermal infrared satellite imagery into actionable insights for agriculture and environmental applications.

Key responsibilities

• Lead, mentor and grow a team of ML engineers focused on Earth Observation data.
• Design and implement robust models for image classification, segmentation, and feature extraction.
• Drive research and development of thermal and multispectral data fusion techniques.
• Collaborate with product and engineering teams to deploy models into production environments.
• Define and enforce MLOps best practices, including model versioning, monitoring, and automated retraining.
• Review model architectures, training pipelines, and code quality to ensure reproducibility.
• Support proposal writing, research collaborations, and external representation of Hydrosat.
• Set team objectives, track key results, and manage hiring, onboarding, and retention.

Required profile

• Master’s degree in Computer Science, Machine Learning, Physics, Mathematics or a related field.
• 10+ years of experience in data science, machine learning or computer vision.
• 2+ years in a leadership role managing ML teams.
• Proven expertise in deep learning architectures such as CNNs, transformers and self‑supervised learning.
• Strong understanding of modern MLOps practices (CI/CD for ML, model serving, experiment tracking).
• Excellent communication and organizational skills in English.

Required skills

• PyTorch
• TensorFlow
• Scikit‑learn
• AWS, GCP, Azure
• Docker
• Kubernetes
• CI/CD pipelines for machine learning
• Model serving and monitoring
• Experiment tracking tools