3 months ago
Responsibilities
- Build perception algorithms and architecture for safe autonomous heavy-equipment operations.
- Adapt and extend existing autonomous-vehicle perception algorithms and develop novel multimodal perception algorithms.
- Retrain models as the operational domain evolves.
- Develop auto-annotation and auto-labeling tools using state-of-the-art methods such as VLMs.
- Define evaluation protocols that correlate with on-ground performance.
- Drive active learning by selecting appropriate data.
- Integrate perception systems with MLOps for continuous deployment.
Requirements
- M.S. or higher in Computer Science, Computer Engineering, Robotics, Electrical Engineering, or a related technical field.
- At least 3 years of applied machine learning experience with large-scale datasets.
- Strong Python and PyTorch skills.
- Experience shipping perception or machine learning systems into production.
- Ability to understand data, models, and infrastructure as one system.
- Experience with VLM-based auto-annotation, model evaluation beyond single metrics, and perception tasks including detection, segmentation, depth, and tracking.
- Experience with multimodal camera, LiDAR, and radar data.
- Dataset management and slicing, synthetic data or simulation, and large-scale data pipelines are preferred.
Categories
ML EngineeringRobotics
