We are looking for a versatile Applied ML Engineer to drive machine learning and perception tasks for edge-intelligent systems in challenging environments. You will handle everything from computer vision and sensor fusion models to lightweight inference pipelines and production fine-tuning. This role offers variety across AI subfields, working with cross-functional teams to deliver robust, efficient solutions.
Key Responsibilities:
- Design, train, and evaluate models for object detection, classification, anomaly detection, and sensor-based inference.
- Optimize model architectures and inference pipelines for embedded and edge hardware with compute and bandwidth constraints.
- Contribute to dataset development, including labeling strategies, data augmentation, synthetic data generation, and domain adaptation.
- Prototype and experiment across computer vision, signal processing, and multi-modal fusion.
- Implement real-time pipelines for sensor data processing on-device and in the cloud.
- Develop tools and scripts for benchmarking, data visualization, and debugging ML performance.
- Stay current with ML research and evaluate applicability to product needs.
4-10 years of experience in applied machine learning, building and deploying production models.
Experience shipping production ML models in domains like computer vision, signal processing, anomaly detection, or sensor data.
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
Held a staff or senior lead role with architectural ownership.
Proven track record at a startup or newer tech company.
Must be eligible to obtain and maintain a security clearance.
Preferred:
Master’s or PhD in Computer Vision, Machine Learning, Robotics, or related field; Bachelor’s considered case-by-case.
Experience with edge or embedded ML deployments, including model compression and hardware-aware optimization.
Familiarity with diverse data types like images, time-series, geospatial, and RF.
Background in regulated industries, preferably government or defense.
History of academic publications in relevant ML fields.