Netradyne is looking to solve problems at the intersection of deep learning, computer vision, and edge AI to revolutionize the modern-day transportation ecosystem and improve fleet safety solutions.
Requirements
- Solid understanding of machine learning and deep learning fundamentals.
- Experience with deep learning frameworks like PyTorch or TensorFlow.
- Strong programming skills in Python; experience with OpenCV, NumPy, and data processing libraries.
- Familiarity with computer vision tasks such as object detection, semantic segmentation, or tracking.
- Experience working with real-world video or sensor datasets.
- Knowledge of edge AI optimization techniques (e.g., quantization, pruning).
- Familiarity with NVIDIA Jetson platforms or similar edge deployment environments.
Responsibilities
- Conduct research and experiments in deep learning, computer vision, and machine learning algorithms.
- Assist in building and optimizing models for object detection, behavior analysis, scene understanding, or sensor fusion.
- Analyze large-scale datasets collected from real-world driving environments.
- Implement and benchmark models using frameworks such as PyTorch or TensorFlow.
- Contribute to prototyping and feasibility studies of new AI features or model architectures.
- Work collaboratively with other researchers, data scientists, and software engineers to integrate models into production pipelines.
- Document findings and present results to the AI and engineering teams.
Other
- Minimum Bachelor's degree, in Computer Science, Electrical Engineering, Artificial Intelligence, or a related field.
- Excellent analytical and problem-solving skills.
- Prior research experience, publications, or relevant projects are a plus.
- Hands-on experience in applied AI research with real-world data.
- Mentorship from senior AI scientists and engineers.
