The mission of the Application Engine Team is to provide a robust, efficient, and flexible platform for integrating and managing various deep learning models and processes in the context of L4 autonomous trucking. It aims to streamline development workflows, enhance team efficiency, and ensure consistent performance and safety standards.
Requirements
- Strong programming skills in C++ and familiarity with Linux development environments.
- Exposure to CUDA, GPU programming concepts, or machine learning frameworks (e.g., PyTorch).
- Internship or project experience involving distributed systems, GPU programming, or embedded software.
Responsibilities
- Develop and maintain components of the App Engine runtime and SDK supporting ML workloads on embedded GPUs.
- Assist in implementing message-passing and data handling between distributed compute nodes.
- Contribute to testing, debugging, and performance tuning of ML integration features.
- Work closely with senior engineers to learn and apply best practices in GPU programming and embedded ML deployment.
Other
- Bachelor’s degree in Computer Science, Electrical Engineering, or related field with 0–4 years of experience, OR Master’s with 0–2 years, OR PhD with 0–2 years.
- Eagerness to learn, contribute, and grow in an applied ML engineering environment.
- A competitive compensation package that includes a bonus component and stock options
- 100% paid medical, dental, and vision premiums for full-time employees
- 401K plan with a 6% employer match
