Design and optimize onboard software systems for deploying large deep learning models in production vehicles, enhancing performance, power efficiency, and latency.

Design and implement tools to enhance the engineering efficiency of ML workflows, complex software quality workflows, and leverage LLMs for better processes.

As a Staff Machine Learning Performance Engineer, you will design and optimize onboard software systems for deploying large deep learning models in production for autonomous driving, focusing on performance and efficiency.

As a Senior Machine Learning Engineer, you will develop innovative deep learning models, optimize neural network architectures, and collaborate with experts in autonomous driving to improve vehicle performance.

Develop big data applications for ADAS, enhance ML platforms, collaborate with engineers, and support system analysis and cloud deployment.

Lead the design and implementation of cloud-native AI infrastructure solutions to support autonomous driving technologies, optimizing performance and reliability.

Design, train, and deploy large deep learning models for autonomous driving using extensive data from a fleet of vehicles.

Design, train, and deploy large deep learning models leveraging extensive data from vehicles to enhance autonomous driving capabilities.

Design, train, and deploy large deep learning models using labeled and unlabeled data from vehicles to solve the autonomous driving problem.

The role involves optimizing AI and ML performance for autonomous driving, focusing on GPU training workloads and reducing inference latency.

The Machine Learning Engineer will fine-tune large language models (LLMs) for robotics and autonomous vehicles, develop training pipelines, and enhance data quality through novel techniques.

The Senior Software Engineer will enhance productivity and CI/CD for autonomy development by designing software solutions and improving workflows.

Seeking a skilled Machine Learning Engineer to enhance productivity through AI methods, mentor engineers, and implement AI solutions in workflows.

Develop big data applications for advanced driving assistance systems (ADAS), collaborating with ML engineers to improve system performance and user support.

As a Data Analyst at XPENG, you will analyze large datasets to identify and resolve edge cases in autonomous driving functionalities, collaborating with engineers to enhance safety and comfort.

The role involves developing machine learning compilers for autonomous driving models, optimizing compilation processes and profiling deployed models.

The role involves researching and implementing deep learning methods for robot motion using reinforcement learning, and requires evaluating and developing advanced control systems.

Research, implement, and evaluate deep learning algorithms for autonomous driving vehicle systems, develop efficient neural network architectures, and work on real-world multimodal data integration.

Develop and evaluate deep learning algorithms for autonomous driving, focusing on end-to-end models that integrate perception, prediction, and planning using multimodal data.

Design and implement large deep learning models for autonomous driving, leveraging vast data sets from vehicles, while enhancing ML infrastructure.