Avride aims to make transportation safer and more efficient through cutting-edge autonomous technology, specifically by developing self-driving taxis and delivery robots. The company is looking to build and deploy an ML-based motion planning stack for outdoor delivery robots operating in complex urban environments.
Requirements
- 3+ years of ML engineering experience or a PhD in a related field.
- Strong Python skills and experience with PyTorch.
- Knowledge of modern C++ and a solid understanding of high-performance code design.
- Solid understanding of machine learning fundamentals and ability to design, train, and evaluate ML models end-to-end — including data preparation, training pipelines, and validation.
- PhD in Computer Science, Machine Learning, Robotics, or a related field.
- Experience in ML-based motion planning or related robotics problems.
- Experience with robotics simulation tools or custom data-driven simulators.
Responsibilities
- Develop closed-loop behavioral models of agents using behavioral cloning and related learning techniques. Build a simulation framework that uses these models to generate realistic multi-agent interactions.
- Design, train, and evaluate ML-based motion planner that operate safely and efficiently in real-time environments. Define evaluation metrics and run large-scale experiments in both simulation and live-ride testing.
- Collaborate with planning, simulation, and perception teams to integrate your models into the full autonomy stack.
Other
- Candidates are required to be authorized to work in the U.S.
- The employer is not offering relocation sponsorship.
- Remote work options are not available.
