On the Motion Planning team at Zoox, you’ll have the unique opportunity to tackle the cutting-edge problem of improving the robot's driving behaviors on public roads. Specifically, within the Mission Progress team, you will focus on one of the most open-ended problems: pickup and dropoff. Pickup/dropoff can range from simple maneuvers on quiet suburban streets to incredibly complex and ambiguous driving scenarios that even humans have difficulty navigating, like airports and unstructured hotel entrances.
Getting pickup/dropoff maneuvers to be safe and reliable is not only a technical challenge but also a way for robotaxi services to eventually distinguish themselves in their user experience. Pickup/dropoff is the first and last thing a rider experiences and has the potential to leave lasting impressions on the rider. This means you will work closely with UI/UX and product to engineer solutions that are not just safe but elegant in how they handle the different human considerations in the pickup/dropoff process.
In this role, you will:
- Design principled algorithmic improvements across different driving behavior categories and implement those changes in our code base.
- Use our extensive backend tooling to test your changes across many different driving scenarios at the press of a button.
- Lead on-vehicle drive reviews to generate actionable feedback on driving performance. Use this experience to help the team learn and make improvements.
- Attend conferences and survey literature to stay informed on the most recent developments in the fields of Robotics and Motion Planning.
- Write and file patents for novel technologies you develop.
Qualifications
- B.S or M.S. degree in Computer Science, Mechanical Engineering, or related field and 8+ years of experience
- Fluency in C++
- Understanding of configuration spaces and a variety of planning techniques (A*, RRTs, PRMs, etc.)
- Demonstrated ability to create real-time motion planning algorithms
Bonus Qualifications
- Experience working with planning problems related to pickup/dropoff or pullover maneuvers
- Experience with machine learning applications in motion planning
- Significant contributions to geometric- and/or sampling-based planning algorithms
$242,000 - $333,000 a year
Base Salary Range
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
Accommodations
If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
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