Applied ML Engineer

Master’s or PhD in Computer Vision, Machine Learning, Robotics, or related field (Bachelors considered case-by-case). 4+ years of experience building and deploying machine learning models. Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow. Comfortable working with a range of data types (images, time-series, geospatial, RF, etc.). Experience with edge or embedded ML deployments, including model compression and hardware-aware optimization. Familiarity with common ML practices including cross-validation, hyperparameter tuning, and model monitoring. Excellent debugging, experimentation, and problem-solving skills. Strong collaboration and communication skills.

Design, train, and evaluate models for object detection, classification, anomaly detection, and sensor-based inference. Optimize model architectures and inference pipelines for embedded/edge hardware. Contribute to dataset development and labeling strategy. Support prototyping across computer vision, signal processing, and multi-modal fusion. Implement real-time pipelines for sensor data processing. Develop tools for benchmarking, visualization, and debugging ML model performance. Stay current with ML research and evaluate applicability. Participate in code reviews, knowledge sharing, and documentation.