Senior Machine Learning Engineer

We are seeking an experienced and versatile AI / Machine Learning Engineer to lead the design, development, and deployment of intelligent algorithms for smart livestock monitoring.
In this role, you will primarily work with multidimensional IMU sensor data (3-axis Accelerometer/Gyroscope) from smart neck collars to classify animal behaviors (e.g., rumination, feeding, resting, estrus, and health anomalies). You will also help shape our end-to-end data architecture and collaborate on integrating computer vision and Edge AI capabilities as our platform expands.

Key Responsibilities
Time-Series ML Modeling:

• Design, train, evaluate, and optimize machine learning and deep learning models (e.g., 1D-CNN, RNN/LSTM, Transformers, XGBoost) on multivariate IMU (x,y,z) sensor streams.
• Extract meaningful frequency- and time-domain features (FFT, wavelet transforms, signal processing) from noisy raw sensor signals.
• Develop behavioral pattern recognition algorithms to detect critical events (heat detection, rumination time, feeding disorders, lameness).

Data Engineering & Architecture:

• Define data structures, ingestion pipelines, and pre-processing workflows for high-frequency sensor streams.
• Collaborate with the hardware and backend teams to ensure scalable, clean, and well-labeled datasets for continuous training and validation.

Computer Vision & Multi-Modal Analytics (Future Expansion):

• Support the integration of vision-based models for camera-assisted farm analytics (e.g., animal tracking, body condition scoring, gait analysis).

Edge AI & Hardware Collaboration:

• Work closely with embedded hardware engineers to optimize models for low-power microcontrollers (MCUs) or processors featuring AI acceleration (TinyML / Edge AI).
• Quantize and compress models (TensorFlow Lite for Microcontrollers, Edge Impulse, ONNX, etc.) to run on-device with minimal power consumption.

Required Qualifications
Education: PhD or Master’s degree in Computer Science, Artificial Intelligence, Electrical/Computer Engineering, or a related field.
Experience: 4+ years of hands-on experience developing and deploying machine learning models in production or applied research.
Core ML & Time-Series Expertise:

• Strong foundation in time-series analysis, signal processing, and sequence modeling.
• Proficiency with Python and modern ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Demonstrated experience in feature engineering from raw sensor feeds (IMU / Accelerometer / Gyroscope).

Data Science & Engineering:

• Solid grasp of database architecture (time-series databases like InfluxDB/TimescaleDB is a plus), ETL pipelines, and data validation best practices.
• Strong problem-solving skills and a solid background in probability, statistics, and linear algebra.

Preferred / Nice-to-Have Skills
Computer Vision: Experience with modern vision architectures (YOLO, OpenCV, CNNs) for object detection, segmentation, and tracking.
Embedded AI / TinyML: Hands-on experience deploying quantized neural networks on constrained hardware (STM32, ESP32, Nordic, or AI-accelerated MCUs/SoCs).
AgriTech / Wearables Experience: Prior domain experience in precision livestock farming, animal behavior monitoring, or human activity recognition (HAR).
MLOps: Experience with model versioning, tracking, and automated deployment pipelines (MLflow, Docker, CI/CD).

What We Offer

• Opportunity to lead the core AI architecture of an innovative, high-impact IoT & AgriTech product from the ground up.
• Collaborative environment working at the intersection of biology, embedded hardware, and machine learning.
• Competitive compensation package and flexible working conditions.