Design, train, and evaluate models for tasks ranging from object detection and classification to anomaly detection and sensor-based inference.
Optimize model architectures and inference pipelines for performance on embedded/edge hardware under compute and bandwidth constraints.
Contribute to dataset development and labeling strategy, including data augmentation, synthetic data generation, and domain adaptation.
Support prototyping and experimentation across a variety of AI subfields, including computer vision, signal processing, and multi-modal fusion.
Implement real-time pipelines for processing sensor data on-device and in cloud environments.
Develop tools and scripts for benchmarking, data visualization, and debugging ML model performance.
Stay current with the latest research and tools in machine learning and evaluate their applicability to our product roadmap.
Participate in code reviews, team knowledge sharing, and internal technical documentation.
Must be eligible to obtain/maintain a security clearance.

Master’s or PhD in Computer Vision, Machine Learning, Robotics, or related field. Bachelors candidates considered on a case by case basis.
4+ years of experience building and deploying machine learning models in production environments.
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 with both technical and non-technical team members.
Bonus: experience in maritime, aerospace, or other remote sensing domains.

Applied ML Engineer

Key skills