Tech Stack

Description

In this role, you will architect and scale ML systems powering a geospatial intelligence platform, transforming satellite imagery into actionable insights. You'll lead end-to-end ownership from model deployment to infrastructure design, collaborating with data science and engineering teams.

Requirements

• 5+ years machine learning engineering with 2+ years in senior/lead capacity
• Proven track record deploying ML systems in production on AWS
• Hands-on experience with MLflow, WandB, or similar experiment tracking tools
• Deep expertise in image segmentation and computer vision with PyTorch or TensorFlow
• Production experience with ensemble models (XGBoost, LightGBM, Random Forest)

Responsibilities

• Design and build end-to-end ML pipelines for batch processing of satellite and aerial imagery
• Deploy and scale ML models on AWS using SageMaker, Bedrock, or custom solutions
• Implement model monitoring, drift detection, and alerting systems
• Design canary deployments and A/B testing for safe model rollouts
• Build active learning pipelines to improve model performance with minimal labeling