At udelv we are creating a new type of driverless vehicle. In January 2018 udelv successfully accomplished the first ever autonomous commercial “last mile” delivery made on public streets using advanced transportation and autonomous driving technologies to revolutionize this segment of transportation.
As a Motion Planning Engineer, you will be responsible for the development of environment modeling, motion planning, and behavioral prediction algorithms, and software that enables an autonomous vehicle to interact with its environment. You will have the chance to work with software engineers implementing these algorithms on highly capable, next generation hardware that will allow you to explore algorithms that were traditionally too costly on edge computing systems.
- Building/Integrating software and algorithms for path planning, behavioral planning and vehicle control
- Testing algorithms in simulation, in vehicle in controlled environment, and ultimately in in vehicle in the field.
- Benchmarking and validating navigation systems for final deployment
- Developing safety engineering and fault tolerance into navigation and control systems.
- Collaborating with technical and non-technical team members to build full-stack autonomous solutions
- Performing other duties as necessary for completion of projects and achievement of goals.
- All other duties as assigned
- MS or PhD degree in CS/CE/EE/ME/Robotics or equivalent with focus on Motion/Path Planning or related field
- Publications in the field of Motion/Path Planning
- Understanding of configuration spaces and a variety of planning techniques (A*, D*, RRTs, PRMs, Convex Programming, Probabilistic Planning, etc.)
- Highly skilled in motion planning and control theory (e.g., model predictive control, vehicle dynamic modeling)
- Extensive experience with programming and algorithm design
- Experience working with large data sets
- Extensive software development experience (C/C++, Python)
- Experience with software engineering tools and linux environment (e.g., Git, CMake, CI, gdb,etc)
- Proven ability to program real-time hardware to execute computationally intensive algorithms
- Experience in Automotive Industry will be regarded. Experience in writing safety-critical code
- Familiarity with ROS and/or other message networking middleware
- SLAM / Probabilistic Filtering experience
- Reinforcement learning experience
- Experience with real-time sensor fusion (e.g. IMU, lidar, camera, odometry, radar)