• MS/PhD or Bachelors degree with a minimum of 4 years of industry experience in Computer Science, Machine Learning and/or similar technical field(s) of study
• Solid knowledge in performance evaluation and optimization of deep learning or computational science algorithms in the areas of GPU kernel development and optimization
• Experience with parameter and architecture tuning of deep learning algorithms
• Solid coding proficiency and knowledge of Python and C++ internals including scientific computing libraries
• Some experience in reading and developing production quality software, versus only creating prototypes/proof of concepts
• Demonstrable track-record of learning and deep-diving as needed into complex existing and new technologies within autonomy systems (i.e., perception, prediction, motion planning and control)
• Open-minded and collaborative team player with the willingness to help others
• Passionate about self-driving technologies, solving hard problems, and creating innovative solutions
• Expert level knowledge of one or more of the following: TensorFlow and PyTorch

Desirable

• Contribute to the autonomy stack for self-driving initiatives by bringing innovative state-of-the-art deep learning models that will enable safe self-driving at scale
• Work on a number of end-to-end autonomy engineering tasks from conceptualization, design, implementation to validation as it relates to cutting-edge algorithmic solutions used to make self-driving a reality. This encompasses core areas of autonomy stack (i.e., prediction, perception, motion planning and control)
• Develop in-depth understanding of deep learning models and algorithms and contribute to optimizing their training protocols, test-time performance, runtime, memory footprint, as well as power consumption
• Identify, propose and build infrastructure, data and efficient pipelines, data storage strategies, common libraries and useful tools needed to improve research and development of deep-learning models
• Design and implement tools for automated model tuning and hyper-parameter optimization, as well as experiment analysis
• Research, validate and incorporate emerging machine learning and research infrastructures, tools, and technologies