Robotics Engineer

Robotics Engineer - Research

Our client's goal is to develop safe and intelligent androids that augment human labor. They have prioritized designing products suitable for mass production to meet global labor needs. The flagship product supports tasks ranging from security to logistics, while the bipedal model is tailored for household use and versatile applications. Equipped with natural language and spatial understanding, the androids excel in diverse environments, completing tasks efficiently.

This Robotics Engineer position entails crafting comprehensive infrastructure spanning simulation, data administration, and learning, alongside devising algorithms aimed at enhancing the sample efficiency of our visuomotor policies. We seek seasoned professionals proficient in training deep neural networks for manipulation, navigation, and locomotion on actual robotic hardware.

Robotics Engineer Responsibilities:

• Expand the scale of datasets utilized for training encompassing end-to-end navigation, manipulation, and locomotion.
• Enhance the functionality of the "data engine," encompassing both frontend UI and backend, facilitating the review, cleaning, and labeling of data.
• Conduct training and evaluation of mobile manipulation policies within simulation environments.
• Mitigate the simulation-to-reality disparity to streamline development iteration cycles.
• Collaborate with the robot operations team to amplify datasets and model capacities.

Robotics Engineer Requirements:

• Bachelor’s degree in Computer Science or its equivalent.
• Accumulate 5 or more years of practical experience in the realm of robotic machine learning.
• Showcase published research in esteemed machine learning conferences such as CVPR, ICML, ICLR, among others.
• Demonstrate proficiency in navigating sizable Python codebases, coupled with adeptness in testing methodologies.
• Exhibit the capability to swiftly translate ideas into code autonomously.
• Display familiarity with concepts of linear algebra and supervised machine learning.
• Demonstrate hands-on experience with popular Deep Learning Frameworks such as Pytorch, TensorFlow, JAX, etc.
• Possess quick thinking and learning abilities.
• Capable of conducting high-caliber machine learning research, yet adaptable to transitioning to tasks related to data infrastructure and operationalizing fleet learning infrastructure as required.