AI Algorithm Team Leader

An exceptional, world-class global aerospace intelligence corporation driving the future of the "New Space" era. The company engineers an advanced, automated satellite operations and space-based intelligence platform.

This sophisticated infrastructure orchestrates everything from automated imagery collection pipelines to autonomous geospatial data analysis utilizing cutting-edge Computer Vision, Machine Learning, and Deep Learning architectures

Composed of approximately 150 elite professionals globally, the enterprise delivers high-fidelity, actionable planetary intelligence to defense, commercial, and governmental entities worldwide

Position Overview

• Computer Vision / AI Algorithm Tech Lead taking professional leadership, technical ownership, and mentorship responsibility over an elite squad of algorithm engineers.
• Steering the end-to-end (E2E) research, design, and productization of advanced Computer Vision and Deep Learning models tailored for high-resolution satellite imagery and complex geospatial data streams.
• Architecting next-generation capabilities by evaluating and integrating multi-modal architectures, Vision-Language Models (VLMs), Large Language Models (LLMs), and object-detection frameworks.
• Driving rigorous research methodologies, experimental standards, and performance evaluation metrics, while maintaining a broad, system-level perspective to bridge raw algorithmic research with live production pipelines.

Requirements

• Academic Background: B.Sc. in Computer Science, Electrical Engineering, or a closely related quantitative discipline – Mandatory
• Extensive, proven professional experience developing advanced Computer Vision algorithms within product-driven environments – Mandatory
• Demonstrated track record of providing significant technological leadership, technical management, or mentoring algorithm engineers – Mandatory
• Deep hands-on experience training and deploying modern Deep Learning architectures (e.g., YOLO variants) – Mandatory
• Strong technical familiarity or practical experience working with Vision-Language Models (VLMs) and Large Language Models (LLMs) – Mandatory
• Thorough understanding of structured research methodologies, rigorous experimentation frameworks, and algorithmic performance evaluation – Mandatory
• Academic Background: M.Sc. or Ph.D. in Computer Science or data-heavy engineering fields – Significant Advantage