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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Machine Learning Engineer, U.S. based - **Company:** SGL - **Location:** Waddinxveen, Netherlands - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Big Data, Software as a Service, Cloud Computing, Information Engineering, Software Debugging, Decision Support Systems, Python (Programming Language), Machine Learning, Object Detection, PostGIS, Software Engineering, Management of Software Versions, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Deep Learning, Model Validation, Data Lakes, Scikit Learn, Information Technology, Geospatial Data Abstraction Library (GDAL), Xgboost, Machine Learning Operations, Software Version Control, Data Pipelines, Raster Graphics, Unsupervised Learning - **Published:** May 15, 2026 - **Apply:** https://nl.indeed.com/viewjob?jk=d8bbfc042d8d321c ## About the Role Do you have experience in SaaS?, * 5+ years of professional experience in machine learning, data science, computer vision, software engineering, or a related technical field. * Strong Python experience. * Strong foundation in computer science, machine learning fundamentals, model evaluation, validation techniques, and ML best practices. * Experience building, training, evaluating, and shipping machine learning models. * Experience with computer vision, image models, or deep learning for visual data. * Experience working with data pipelines, feature engineering, and large datasets. * Familiarity with MLOps concepts such as experiment tracking, model versioning, deployment, monitoring, retraining, and reproducibility. * Ability to work independently, ask good questions, and take ownership of ambiguous technical problems. * Comfort working in a startup or high-velocity product environment., * Experience with geospatial systems, remote sensing, drone imagery, multispectral imagery, raster/vector data, photogrammetry, or GIS workflows. * Experience with computer vision workflows such as image classification, semantic segmentation, object detection, and change detection, especially for aerial, drone, satellite, multispectral, or other geospatial imagery. * Experience with vegetation indices, spatial statistics, time-series analysis, environmental datasets, or agricultural datasets. * Experience with self-supervised learning, unsupervised learning, weak supervision, active learning, or human-in-the-loop model improvement workflows. * Experience with LLMs, agents, context engineering, MCP, retrieval-augmented generation, or AI-assisted tooling for prototyping, research, development, testing, documentation, or delivery. * Experience with cloud infrastructure and AWS-based data or ML workflows. * Experience with PyTorch, TorchGeo, Raster Vision, scikit-learn, XGBoost, LightGBM, GeoPandas, rasterio, GDAL, PostGIS, or similar ML/geospatial tools. * Experience building ML features for production SaaS products. * Experience in sports turf, agriculture, environmental monitoring, construction, oil and gas, or other remote sensing use cases. ## Description We are seeking an AI/ML Engineer to help build the next generation of TurfBase. This role is well suited for a mid-level to senior engineer who enjoys solving real-world machine learning problems across computer vision, geospatial analytics, data pipelines, and production SaaS systems. You will help activate our growing data lake of drone imagery, multispectral data, geospatial layers, turf performance history, sensor data, and agronomic observations. Your work will span research, experimentation, model development, productionization, and continuous improvement of customer-facing AI features. This is a high-ownership role with direct impact on the intelligence layer of the platform. You will work closely with software engineering, product, agronomy, and leadership to improve how TurfBase detects turf stress, prioritizes issues, tracks change over time, supports customer decision-making, and accelerates future AI-powered capabilities. We are building a modern, high-velocity software team in Pittsburgh and value engineers who thoughtfully use AI-assisted tools to accelerate research, prototyping, implementation, testing, debugging, and delivery. The position requires on-site collaboration five days per week., * Develop, train, evaluate, and maintain machine learning models for imagery, geospatial, time-series, and sensor-based workflows. * Build computer vision models for turf intelligence use cases such as image classification, semantic segmentation, change detection, issue prioritization, and stress detection. * Help activate our data lake by identifying useful training signals, feature sets, labeling strategies, and model opportunities across historical and incoming datasets. * Research and prototype classical ML, deep learning, self-supervised learning, unsupervised learning, weak supervision, and active learning approaches. * Design practical experiments, evaluate model performance, and translate research findings into production-ready product improvements. * Productionize and maintain models in a SaaS environment, including deployment support, model monitoring, retraining workflows, versioning, and reproducibility. * Build and improve data pipelines for imagery, geospatial, tabular, time-series, and sensor data. * Collaborate with software engineering, product, agronomy, and leadership to integrate ML outputs into customer-facing TurfBase workflows. * Explore practical uses of LLMs, agents, context engineering, and AI-assisted development tools to improve internal productivity and future product capabilities. Your profile We are looking for a pragmatic, product-minded AI/ML Engineer who enjoys turning messy real-world data into useful production systems. You should be comfortable moving between research, experimentation, data engineering, model development, and product implementation. You do not need to be a turf expert on day one, but you should be excited by the opportunity to apply AI and machine learning to real-world physical systems involving drone imagery, remote sensing, geospatial analytics, and agronomic decision support. ## Related Videos - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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