Computer Vision Engineer

About us:
Farsight Vision develops AI-powered geospatial intelligence solutions for defence and security operations. The platform transforms raw aerial and sensor data into real-time 3D environments, actionable analytics, and AI-assisted operational scenarios, enabling enhanced situational awareness, mission planning, and faster decision-making for both human operators and autonomous systems. We operate at the intersection of geospatial data, computer vision, and defense operations – and if that interests you, read on.

Responsibilities:

• Design, develop, and deploy production-ready computer vision algorithms for image and video analysis in GIS-related applications
• Build end-to-end perception pipelines – data collection, preprocessing, model development, deployment, and monitoring
• Integrate CV components into operational products in close collaboration with cross-functional engineering teams
• Optimize algorithms for performance, reliability, and scalability in real-time and resource-constrained environments
• Develop and deploy Visual Place Recognition (VPR) systems for geo-localization and scene understanding tasks
• Implement and optimize CV algorithms on embedded and hardware-accelerated platforms, ensuring stable and efficient inference at the edge
• Process and analyze large-scale datasets for model training, validation, and continuous improvement
• Diagnose and resolve complex issues across computer vision applications in production
• Contribute to internal research, exploring emerging techniques in image synthesis, manipulation, and applied ML.

Requirements:

• Expert-level Python – this is our primary engineering language. You must be comfortable writing clean, efficient, production-quality Python code
• 5+ years of hands-on experience in computer vision and applied machine learning
• Deep working knowledge of core CV and ML libraries: OpenCV, PyTorch, TensorFlow, and related tooling
• Solid understanding of machine learning, deep learning, and model development workflows end-to-end
• Hands-on experience with Visual Place Recognition (VPR) algorithms and techniques, including descriptor-based retrieval, sequence matching, and localization under varying conditions
• Proven ability to port, optimize, and run CV/ML models on embedded devices and edge hardware (e.g., NVIDIA Jetson, Raspberry Pi, or similar platforms)
• Proven experience with image processing, model training, and evaluation methodologies
• Ability to translate research into reliable engineering outputs with minimal supervision

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field – or equivalent demonstrable experience

• Strong analytical thinking and clear communication in a cross-cultural, fast-moving team.

Would be a plus:

• Experience with graphics APIs: OpenGL, OpenGLES, or Direct3D
• Background in graphical algorithms, 3D rendering, or computational geometry
• Familiarity with embedded systems or hardware-accelerated proces