About the job
Join Our Innovative Team
At avride, we are at the forefront of revolutionizing transportation through our cutting-edge autonomous vehicle technology. Our dedicated team is responsible for creating the essential software and data processing frameworks that enable motion planning and decision-making in self-driving cars. We thrive at the crossroads of machine learning, extensive data infrastructure, and real-time vehicle control, collaborating closely with engineering, analytics, and product teams to provide safe and intelligent driving solutions.
Position Overview
We are seeking a passionate and innovative Machine Learning Engineer to join our autonomous vehicle division. In this pivotal role, you will contribute to the development of intelligent systems capable of comprehending, predicting, and safely navigating the complexities of the real world. Your responsibilities will encompass designing and training advanced deep learning models that serve as the cognitive core of our vehicles, utilizing vast amounts of real-world driving data. If you have a keen interest in leveraging state-of-the-art ML techniques to tackle high-stakes robotics challenges, we encourage you to apply.
Key Responsibilities
- Develop, train, and implement advanced machine learning models for behavioral prediction and motion planning.
- Create efficient data pipelines to process, cleanse, and annotate large-scale vehicle sensor and simulation datasets.
- Utilize deep learning architectures such as transformers to capture intricate temporal interactions among traffic participants.
- Define and manage metrics for model performance, establishing evaluation frameworks that align with on-road safety and efficacy.
- Collaborate with software engineers to integrate and optimize trained models for real-time inference on vehicle hardware.
- Keep abreast of the latest advancements in machine learning, imitation learning, and reinforcement learning, applying innovative methodologies to our systems.
Qualifications
- Expertise in Python with practical experience in modern deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX).
- Strong grasp of machine learning principles, including various neural network architectures, training techniques, and evaluation methods.
- Experience across the complete machine learning lifecycle, from data exploration and model prototyping to deployment and monitoring.
- Proficient in C++ for developing high-performance inference code for models.
Preferred Qualifications
- Demonstrated success in ML competitions (e.g., Kaggle) or significant contributions to prominent open-source ML projects.
- Experience applying machine learning to real-world problems in robotics or autonomous systems.
