We’re looking for a talented and driven Mobile AI Engineer to lead the development of an
on-device plant stalk recognition system.

This role spans the full machine learning pipeline — from dataset curation to mobile deployment — with a strong emphasis on edge AI and real-time inference on Android and iOS.

You’ll be working on a critical product aimed at improving precision agriculture by enabling
fast and accurate recognition of single plant stalks (e.g. corn, wheat) using mobile devices.

Responsibilities

• Design, train, and optimize lightweight computer vision models (e.g. MobileNetV3, YOLOv5n) for plant stalk detection and classification.
• Build efficient preprocessing and data pipelines (TFRecords, augmentation)
• Perform quantization-aware training and model compression for TFLite or Core ML deployment
• Develop a production-grade mobile app using any cross-platform or native framework (e.g. Flutter, React Native, Kotlin Multiplatform, Swift, etc.)
• Integrate live camera feed and real-time inference on-device
• Benchmark and profile inference accuracy and performance on mobile hardware
• Write reusable scripts for annotation, training, and model export

Required Skills

• Mobile App Development:
o Proficiency in developing cross-platform or native mobile apps (Android and iOS)
o Experience integrating ML models (TFLite, Core ML, ONNX) into mobile apps
o Familiarity with real-time image capture and display
• Machine Learning / Computer Vision:
o Solid understanding of TensorFlow/Keras and model training lifecycle
o Experience with efficient image classification and object detection models
o Knowledge of quantization, pruning, and model optimization for edge devices
• Deployment & Debugging:
o Ability to profile mobile performance and optimize latency and memory usage
o Experience testing across device variations (Android/iOS, CPU/GPU)

Bonus Skills

• Background in agriculture, biology, or experience with related datasets
• Experience with AutoML or Neural Architecture Search (NAS) for mobile
• Familiarity with OpenCV and plant structure segmentation

Tools & Technologies

• Python, TensorFlow, Keras
• ONNX, TFLite, Core ML
• Mobile frameworks: Flutter, React Native, Swift, Kotlin Multiplatform, etc.
• Android Studio / Xcode
• Git, GitHub Actions (CI/CD)

What We Offer

• The opportunity to work on a meaningful AI product in the agriculture domain
• Flexible, remote-first work culture
• Real-world data and support for field testing
• Competitive compensation

How to Apply

Hit the apply button on this page and send your CV, GitHub or portfolio, and a brief note explaining your interest in the project.

Please include links to any mobile AI apps or ML projects you’ve worked on.