AI Machine Learning Engineer

About SGL

At SGL, we do not just grow grass; we help grounds teams create the perfect pitch for the world’s biggest sports stadiums. Since developing our revolutionary turf optimisation system in 2002, we have become the market leader in sports turf technology, supporting grounds teams in over 600 stadiums worldwide. Our technology helps the teams responsible for the quality of the playing surface grow and maintain world-class turf. We do this through innovations such as high-tech grow lighting systems, smart data monitoring tools, and sustainable grass disease management solutions, such as our autonomous UVC robot. With an international team of more than 70 passionate specialists, we continue to push the boundaries of professional sports turf innovation.

TurfBase

TurfBase is SGL’s next-generation software platform for turf intelligence. TurfBase brings together drone data, geospatial analytics, IoT sensors, robotics, and cloud-based software into a unified system that helps teams monitor, analyze, and optimize turf performance at scale. From our office in Cranberry Township / Wexford, Pennsylvania area we are working on our platform with a very ambitious team.

SGL as an employer

Working at SGL means playing in the big league. Our team thrives in an informal culture where collaboration, innovation, passion and personal development go hand in hand. We encourage learning, sharing ideas, and taking ownership of your work, so every team member can make an impact. Great work comes from great teams – from Friday drinks and table tennis to team outings that bring colleagues from across the globe together, we make sure there is plenty of fun and connection. At SGL, teams bond in ways that matter to them!

The position

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.

Key responsibilities

• 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.

Your qualifications

• 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.

Preferred skills

• 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.

What we offer

At SGL, you’ll join an ambitious team in a dynamic international work environment, developing innovative and unique products used worldwide. This is a role with high responsibility, room for personal growth, and a great working atmosphere.

Our benefits

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

We strongly value personal development, and you will have the opportunity to take job-related training to improve your skills and help elevate our team.

Interested?

Would you like more information? Please contact Diederick at 06 103 050 94

Interested? Send us your CV and motivation letter (optional) at hiring@sglsystem.com. We will get back to you as soon as possible.

Acquisition in response to this job posting is not appreciated.