Machine Learning Engineer 2, ML App and Frameworks
About The Position
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.
Requirements
- Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus 2 years of experience as a Machine Learning Engineer.
- Will accept 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.
- Experience must include include 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; designing and implementing ML lifecycle pipelines (training, testing, deployment).
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.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
501-1,000 employees
