Remote Sensing Machine Learning Engineer

Kumi Analytics Pte Ltd; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Kumi Analytics Pte Ltd is seeking a Remote Sensing Machine Learning Engineer to develop proprietary AI/ML algorithms that extract advanced insights from multi-sensor satellite data. You will design and train models for object detection, segmentation, and classification, processing large-scale Optical, SAR, and hyperspectral data.

The role requires 3+ years in ML for computer vision or remote sensing, strong Python and DL framework skills, and experience with Earth Engine.

Qualifications

• 3+ years in ML for CV or remote sensing.
• Strong proficiency in Python and DL frameworks (PyTorch, TensorFlow).
• Experience with multi-sensor satellite data (SAR, Optical) and Earth Engine.
• Solid ML lifecycle knowledge from data prep to deployment.
• Start-up environment experience preferred.

Responsibilities

• Design and train deep learning models for object detection, segmentation, and classification on satellite imagery.
• Process and analyze large-scale Optical, SAR, and hyperspectral data.
• Optimize ML models for cloud deployment (AWS/GCP).
• Collaborate with remote sensing and data engineering teams to build scalable ML pipelines.
• Utilize Google Earth Engine and open-source tools for data prep and analysis.
• Opportunity to progress as an individual contributor or into management.

Skills

Machine Learning

Python

PyTorch

TensorFlow

Computer Vision

Remote Sensing

Cloud Deployment

Tools

Google Earth Engine

AWS

GCP

Job description

Remote Sensing Machine Learning Engineer

We’re looking for a mission-driven Remote Sensing Machine Learning Engineer to develop proprietary AI/ML algorithms that extract advanced insights from multi-sensor satellite data.

Specific responsibilities will include:

• Designing and training deep learning models for object detection, segmentation, and classification on satellite imagery
• Processing and analyzing large-scale Optical, SAR, and hyperspectral data
• Optimizing ML models for deployment on cloud infrastructure (AWS/GCP)
• Collaborating with the remote sensing and data engineering teams to build scalable ML pipelines
• Utilizing Google Earth Engine and open-source tools for data preparation and analysis
• Those who are successful will progress quickly, either as individual contributors or following a management path.

We’re looking for someone who has:

• 3+ years of experience in Machine Learning, specifically applied to computer vision or remote sensing
• Strong proficiency in Python and deep learning frameworks (PyTorch, TensorFlow)
• Experience working with multi-sensor satellite data (SAR, Optical) and Google Earth Engine
• Solid understanding of ML lifecycle, from data preparation to model deployment
• Cares deeply about the climate and ecosystems of earth
• Is a self-starter who thrives in constantly evolving environments, ideally with early-stage start-up experience