Machine Learning Engineer at Geosoft – Abu Dhabi | Jobyssey

We are seeking an AI/ML Lead to build and lead our artificial intelligence and machine learning capabilities. This is a hands-on technical leadership role—not a sales or strategy role. You will train models, build pipelines, and lead a team that turns raw geospatial data into automated intelligence. We are looking for someone who has trained AI models, not someone who has sold AI solutions. You will report to the CTO and work closely with the platform engineering and data teams. What You Will Do AI Model Development (Hands-On) • Train and deploy AI models for point cloud classification (pipes, valves, structures, equipment) • Develop semantic segmentation models for 3D environments • Build object detection and recognition for industrial assets • Create anomaly detection models for inspection data (corrosion, deformation, damage) • Develop change detection algorithms for comparing surveys over time Point Cloud & 3D AI (Core Focus) • Build automated point cloud classification pipelines • Develop models for feature extraction from LiDAR and photogrammetry data • Create 3D instance segmentation for complex industrial environments • Train models on as-built vs. as-designed comparison • Build automated modeling from point clouds (scan-to-BIM, scan-to-CAD) GeoAI & Geospatial Intelligence • Develop geospatial analysis models for terrain, infrastructure, and assets • Build satellite and aerial imagery analysis capabilities • Create spatial relationship models for asset intelligence • Integrate GIS data with AI for location-based predictions Data Pipeline & MLOps • Design and build training data pipelines for model development • Establish data labeling workflows and quality standards • Build MLOps infrastructure: model versioning, A/B testing, monitoring • Create automated retraining pipelines for continuous improvement • Manage GPU infrastructure for training and inference Team Leadership • Build and lead AI/ML team (starting 3, scaling to 10+) • Hire and mentor ML engineers and data scientists • Establish AI development best practices and documentation • Manage relationships with annotation teams and data providers Research & Innovation • Stay current with state-of-the-art in 3D deep learning, point cloud processing • Evaluate and adapt open-source models for our use cases • Publish research and represent Geosoft at AI/ML conferences • Build relationships with academic institutions for research partnerships

• 5+ years leading ML teams (even small teams count) • 7+ years hands-on experience in machine learning and deep learning • Proven experience training models from scratch—not just using pre-trained models • Deep expertise in computer vision and/or 3D deep learning • Production ML experience: models you built are running in production today What You Bring Required Experience (Non-Negotiable) AI/ML Technical Depth • 7+ years hands-on experience in machine learning and deep learning • Proven experience training models from scratch—not just using pre-trained models • Deep expertise in computer vision and/or 3D deep learning • Production ML experience: models you built are running in production today Point Cloud & 3D Experience (Critical) • Direct experience with point cloud classification using deep learning • Experience with 3D semantic segmentation and instance segmentation • Understanding of LiDAR data processing and point cloud formats (LAS, LAZ, E57) Domain-Specific • Experience in geospatial AI, GeoAI, or remote sensing ML • Background in industrial asset recognition (oil & gas, utilities, infrastructure) • Experience with photogrammetry or drone imagery analysis • Knowledge of BIM, CAD, or digital twin data structures Leadership • 5+ years leading ML teams (even small teams count) • Experience mentoring junior ML engineers • Track record of taking ML projects from research to production What We Are NOT Looking For • AI/ML sales or pre-sales roles without hands-on model development • Data scientists focused only on tabular data / traditional ML • Pure researchers with no production deployment experience • Managers who don't code anymore Tech Stack: • Python, PyTorch, Open3D • AWS (S3, SageMaker, EC2 GPU instances) • PostgreSQL, MongoDB • Docker, Kubernetes • React/Three.js (for visualization—you won't build this but will integrate with it)

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