Applied AI Researcher

GalaxEye; Arishinakunte; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

A satellite imagery technology firm seeks an Applied AI Researcher to design and evaluate machine learning models for satellite data. The role involves developing advanced models for tasks like image captioning and change detection, collaborating with teams to translate research into practical solutions. Ideal candidates should have expertise in deep learning, Python, and experience with satellite imagery datasets. This is a unique opportunity to bridge research and deployment in a cutting-edge field.

Qualifications

• Hands-on experience with foundation models and vision‑language models (VLMs).
• Experience with large-scale image and multi-modal datasets.
• Ability to analyze results and guide improvements.
• Strong understanding of detection, segmentation, and VLM integration.
• Ability to conduct rigorous experiments and interpret results.

Responsibilities

• Design, train, and fine‑tune models for satellite and geospatial image processing.
• Develop models for detection, segmentation, and vision‑language tasks.
• Collaborate to translate mission needs into practical AI solutions.
• Create scalable pipelines for large geospatial datasets and benchmarking.
• Collaborate with AI engineering, product, and domain teams to translate needs.
• Support transition of research prototypes to production with MLOps.
• Publish findings at AI, CV, and remote sensing forums.

Skills

Deep learning

Computer vision

Machine learning

Python

PyTorch

Collaborative communication

Geospatial data

Experimentation

Collaboration

Satellite imagery

Tools

GIS tools

ML frameworks

Job description

GalaxEye seeks an Applied AI Researcher to design, build, and evaluate advanced machine learning models, focusing on foundation models and vision‑language models (VLMs) tailored for multi‑sensor satellite data. This role bridges research innovation and practical deployment, unlocking intelligence from satellite imagery through state‑of‑the‑art AI techniques.

Responsibilities

• Design, train, and fine‑tune foundation models and VLMs specifically adapted to satellite and geospatial image processing, enabling better semantic understanding and cross‑modal reasoning.
• Develop models for detection, segmentation, change detection, retrieval, and vision‑language tasks such as image captioning and visual question answering on satellite imagery.
• Prototype and experiment with novel architectures and training methods to improve model accuracy, robustness, and efficiency in remote sensing environments.
• Create scalable data processing and experimentation pipelines for large geospatial datasets, ensuring reproducibility and rigorous benchmarking.
• Collaborate with AI engineering, product, and domain experts to translate mission needs into research deliverables and practical AI solutions.
• Support transition of research prototypes to production via close collaboration with MLOps and software teams.
• Publish findings in AI, computer vision, and remote sensing forums; represent GalaxEye in the global research community.

Required Qualifications

• Expertise in deep learning, computer vision, and machine learning with hands‑on experience developing and deploying foundation models and vision‑language models (VLMs).
• Proficiency in Python and ML frameworks such as PyTorch, with specialties in model fine‑tuning for specialized domains like satellite imagery.
• Experience with large‑scale image and multi‑modal datasets, including preprocessing and data augmentation for geospatial applications.
• Solid understanding of object detection, segmentation, metric learning, and vision‑language integration in remote sensing contexts.
• Familiarity or strong interest in satellite data modalities (SAR, multispectral, hyperspectral) and their implications for AI modeling.
• Demonstrable ability to conduct rigorous experiments and analyze results to guide iterative improvements.
• Collaborative communication skills and the ability to work in interdisciplinary teams.

Preferred Qualifications

• Prior experience building or fine‑tuning VLMs or foundation models for complex imaging domains such as satellite or aerial imagery.
• Exposure to geospatial data standards (GeoTIFF, NetCDF) and GIS tools.
• Familiarity with model optimization techniques to enable efficient deployment on cloud or edge platforms.
• Publications or open‑source contributions in AI, vision‑language, or remote sensing research.