L2 Machine Learning Scientist

As an organization at the forefront of autonomous driving, we have gathered vast amounts of valuable sensor data that provide a deep insight into various driving environments and scenarios. Within the AI Foundation Department our strategic response is to integrate foundational machine learning techniques to harness its full potential and drive its capabilities further ahead to adhere to future needs.

Therefore, we are searching for a Deep Learning Specialist with Reinforcement Learning or Control Background deploying our next-generation modular and differentiable AV stack, bridging the gap between traditional model-based modularity and modern deep learning practices.

Key Responsibilities:

• Design and deployment of our innovative modular and differentiable AV stack, focusing on prediction, planning, and control.
• Collaborate closely with our research and development teams, ensuring the seamless incorporation of model-based planning and control algorithms with advancements in differentiable optimization.
• Drive the continuous enhancement of our AV architectures, championing the harmony between modularity and end-to-end neural networks.
• Spearhead the evaluation process of our AV stack, ensuring marked improvements in both open-loop and closed-loop planning metrics.
• Advocate for and ensure the interpretability, reusability, and efficiency of our AV solutions, while maximizing the capabilities of deep learning and differentiable optimization.
• Stay abreast of the latest developments in differentiable optimization, ensuring our AV stack remains at the forefront of innovation.

Qualifications:

• MS, or PhD in Machine Learning, Computer Science, Robotics, or related fields with at least the years working experience or equivalent.
• Comprehensive understanding of modular AV system components combined with proficiency in end-to-end neural network designs.
• Hands-on experience with differentiable optimization and its applications in deep learning models.
• Demonstrable history of deploying neural network architectures for practical applications, with a preference for those within the autonomous vehicles domain.
• Expertise in Python and familiarity with major deep learning frameworks like PyTorch or TensorFlow.
• Exceptional analytical and problem-solving acumen, particularly in optimizing system performance and mitigating integration challenges.

Bonus Points!

• Scholarly publications related to differentiable optimization, neural network architectures, or autonomous vehicle systems in high tier conferences as CVPR, ICCV, ICRA, IROS, NeurIPS ...
• Prior experience in developing or collaborating on AV stacks, especially in prediction, planning, and control sectors.
• Knowledge of contemporary strategies for testing and deploying deep learning models in real-world settings.