AI Machine Learning Engineer, U.S. based

Responsibilities

• check_circle Develop, train, evaluate, and maintain machine learning models for imagery, geospatial, time-series, and sensor-based workflows.
• check_circle Build computer vision models for turf intelligence use cases such as image classification, semantic segmentation, change detection, issue prioritization, and stress detection.
• check_circle Help activate our data lake by identifying useful training signals, feature sets, labeling strategies, and model opportunities across historical and incoming datasets.
• check_circle Research and prototype classical ML, deep learning, self-supervised learning, unsupervised learning, weak supervision, and active learning approaches.
• check_circle Design practical experiments, evaluate model performance, and translate research findings into production-ready product improvements.
• check_circle Productionize and maintain models in a SaaS environment, including deployment support, model monitoring, retraining workflows, versioning, and reproducibility.
• check_circle Build and improve data pipelines for imagery, geospatial, tabular, time-series, and sensor data.
• check_circle Collaborate with software engineering, product, agronomy, and leadership to integrate ML outputs into customer-facing TurfBase workflows.
• check_circle Explore practical uses of LLMs, agents, context engineering, and AI-assisted development tools to improve internal productivity and future product capabilities.
• check_circle 5+ years of professional experience in machine learning, data science, computer vision, software engineering, or a related technical field.
• check_circle Strong Python experience.
• check_circle Strong foundation in computer science, machine learning fundamentals, model evaluation, validation techniques, and ML best practices.
• check_circle Experience building, training, evaluating, and shipping machine learning models.
• check_circle Experience with computer vision, image models, or deep learning for visual data.
• check_circle Experience working with data pipelines, feature engineering, and large datasets.
• check_circle Familiarity with MLOps concepts such as experiment tracking, model versioning, deployment, monitoring, retraining, and reproducibility.
• check_circle Ability to work independently, ask good questions, and take ownership of ambiguous technical problems.
• check_circle Comfort working in a startup or high-velocity product environment.
• check_circle Experience with geospatial systems, remote sensing, drone imagery, multispectral imagery, raster/vector data, photogrammetry, or GIS workflows.
• check_circle 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.
• check_circle Experience with vegetation indices, spatial statistics, time-series analysis, environmental datasets, or agricultural datasets.
• check_circle Experience with self-supervised learning, unsupervised learning, weak supervision, active learning, or human-in-the-loop model improvement workflows.
• check_circle Experience with LLMs, agents, context engineering, MCP, retrieval-augmented generation, or AI-assisted tooling for prototyping, research, development, testing, documentation, or delivery.
• check_circle Experience with cloud infrastructure and AWS-based data or ML workflows.
• check_circle Experience with PyTorch, TorchGeo, Raster Vision, scikit-learn, XGBoost, LightGBM, GeoPandas, rasterio, GDAL, PostGIS, or similar ML/geospatial tools.
• check_circle Experience building ML features for production SaaS products.
• check_circle Experience in sports turf, agriculture, environmental monitoring, construction, oil and gas, or other remote sensing use cases.

Benefits

• check_circle A challenging international working environment.
• check_circle A versatile role in a young and ambitious team.
• check_circle Plenty of responsibility, initiative.
• check_circle Medical, dental, and vision insurance (70% employer contribution / 30% employee contribution).
• check_circle Travel expense reimbursement.
• check_circle Retirement (according company policy).
• check_circle All necessary equipment provided (phone/laptop).
• check_circle Work-related training courses to further develop professional skills.
• check_circle Fun and team-oriented environment.

About the company