Software Engineer
Software Engineer (Job Number: P17F03)
Mountain View, CA

​As part of our System Integration Group, the candidate will be in charge of designing and developing a scalable and reliable software architecture for our Autonomous Driving activities. He/she will also be involved in the research and development of state of the art motion planning and decision making algorithms that will be tested on the Automated Drive vehicle.



  • Understand and analyze state of the art approaches and contribute to algorithm development in the areas of path/motion planning and decision making.
  • Implement low-latency and high performance software modules, integrate, deploy, and test them in our AD platform.
  • Develop software infrastructure and tools to facilitate team's development.
  • Be an essential member of a team of engineers and scientists that develop autonomous driving technologies in a fast-paced software development environment.


  • M.S. (or B.S. with 2+ years experience) in computer science, electrical engineering or related field.
  • Proficient in modern software development tools such as GCC, CMake, Git, CI, Docker..
  • Experience with Robot Operating System (ROS).
  • Expertise in motion planning theory, decision making.
  • Excellent programming skills in C++.
  • Experience in data structures and advanced algorithms.
  • Proven ability to design, develop and debug production quality code.
  • Be self-motivated and able to work well in cross-functional teams.
  • Strong communication skills including technical documentation, written reports, proposals, development and delivery of presentations and the ability to listen and communicate effectively.

Preferred Qualifications:

  • Familiarity with hardwares, including cameras, LiDAR, GPS, CAN, IMU, USB, Ethernet.
  • Knowledge in basic machine learning technology.
  • Extensive hands-on experience in real-world robotics applications.


We compute various musculoskeletal indicators of human performance when the driver is operating a vehicle under normal and emergency maneuvering.
The goal of the project is to achieve robust lane-level localization for cars using low cost mass producible sensors.
We have developed online algorithms to transfer motion from a human demonstrator to Honda's humanoid robot, ASIMO.
The development of the next generation green and safe batteries with high energy density is highly desirable for meeting the rapidly growing needs of electrical vehicles.
Rare earth Manganese Oxide (ReMO) as a cathode material has potential capacity higher than that of commercial lithium manganese oxide.
Metal-air batteries are solid state batteries using metal oxidation at the anode and oxygen reduction at the cathode to induce a current flow.
This project is focused on CNT and graphene enforced electrode materials for secondary battery and supercapacitor applications.
This research is focused on scale-up technology for continuous synthesis of SWNTs by CVD method and the exploration of their performance in actual electrochemical devices
The core of this project is for atomistic level understanding of environmental impact on conductivities of SWNTs and 2-D materials in order to reveal their ultimate sensitivities.
Synthesis and studies of growth mechanism, self assembly and properties of low dimensional nanomaterials for alternative energy technologies are at the core of our research.
The goal is to develop driving aids that enhance the driver's situational awareness and give drivers a sense of confidence and trust in the vehicles they are operating.
In Knowledge Discovery, we Integrate knowledge from multiple sources such as Wikipedia, Yahoo Question/Answers, Open Directory Project and OpenMind.
Probabilistic model to track dialog state and provide information to driver viaspoken dialog
This project presents a control theoretic approach for human pose estimation from a set of key feature points detected using depth image streams obtained from a time of flight imaging device.
We are developing a real-time system that detects and tracks traffic participants.
We aim to develop a robust pedestrian detection algorithm that can handle partial occlussions.
We are taking advantage of our autonomous driving platform to create a comprehensive repository of annotated sensor data that provide computer vision benchmarks and training data to support advanced driving assist and autonomous driving applications.
Backing-up of articulated vehicles poses a difficult challenge even for experienced drivers. While long wheelbase dual-axle trailers provide a benefit of increased capacity over their single-axle counterparts, backing-up of such systems is especially difficult. We devise a control strategy for such systems, allowing backing-up maneuvers to be intuitive to drivers without experience with trailers. Using hitch angle feedback, we show these concepts can be used to stabilize the trailer in back-up motion in the presence of arbitrary driver inputs.
Utilizing the latest wearable sensing technologies and patented motion prediction algorithms, the goal is to predict human movement and perform biomechanical computations based on those predictions.