Design and implement scalable ML systems for Level 4 autonomous driving technology at HQ in Blacksburg, VA
Requirements
- Experience with tensorrt, pytorch, ros and ray based workflows
- Distributed training systems for large-scale ML datasets (petabyte-scale)
- Proficiency in deep learning frameworks (PyTorch, TensorFlow, Keras)
- Model optimization techniques (compression, quantization, ONNX, TensorRT)
- Distributed ML infrastructure for scalable training and orchestration tools (Ray, Dagster,)
- Multimodal systems for autonomous vehicles
- Programming in Python, C++ , Ros middleware
Responsibilities
- Architect scalable ML systems capable of handling petabytes of training data to power Level 4 autonomous driving technology
- Design and implement a unified platform for training and deployment of state-of-the-art machine learning models
- Develop scalable training pipelines to efficiently process massive datasets for autonomous vehicle perception systems
- Create deployment solutions that optimize model performance on edge computing devices
- Build infrastructure to support continuous improvement of models through testing and validation
- Collaborate with cross-functional teams to integrate ML systems with other vehicle systems
Other
- Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus 2 years of experience as a Machine Learning Engineer
- Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competences and technical proficiencies typically acquired through 5 years of experience as a Machine Learning Engineer
- Position is located at HQ in Blacksburg, VA but eligible to work from anywhere in the U.S.
- Collaborate with cross-functional teams
- Work from anywhere in the U.S.
