We are seeking a Principal Applied Scientist to join our industry-leading team developing cutting-edge technologies in multi-robot planning and control.
This is a unique opportunity to play a pivotal role in enabling fleets of autonomous mobile robots to operate safely and efficiently around humans in dynamic real-world environments.
In this position, you will lead projects to push the boundaries of collaborative autonomy - getting teams of robots to cooperate and coordinate to get work done.
You will develop and deploy new planning, coordination and control methods for our next generation of mobile robots. Your solutions will allow packs of robots to navigate warehouses transporting goods, performing services, and taking over dangerous dull dirty tasks - all while safely sharing space with people.
This opportunity offers the chance to have broad impact by enabling robots to take on new roles that will transform how we work.
You will collaborate with brilliant colleagues in a culture that values ingenuity, teamwork and making an impact. Come join our mission to pioneer the future of automation!
About the team
The Movement Science team studies algorithms for efficient motion planning and coordination of Amazon's mobile robots. Our aim is to improve the efficiency of robot movement and to enable adaptation to new operating conditions.
We focus on two key requirements of movement : preserving liveness and working at scale. We are driven by system-level problems, where a new algorithm can unlock a new solution design, fix a significant shortcoming, guarantee performance under varying conditions, or improve how complex tradeoffs are made.
As a science team, we take on projects which require cutting-edge research, a longer time-horizon, or carry significant risk.
BASIC QUALIFICATIONS
- PhD degree in CS, CE, or related technical field
- 10+ years of relevant, broad research experience after the PhD degree
- Experience programming in C++, Java, Python or related language
- Experience using state of the art robotics development toolchains, e.g., ROS and Gazebo
- Strong publication record with contributions to leading conferences / journals
PREFERRED QUALIFICATIONS
- Extensive experience developing and deploying algorithms for multi-robot coordination in real-world settings
- Deep knowledge of adjacent fields, including perception, localization, control, motion planning, and optimization for autonomous robots
- Extensive experience translating research innovations into fielded robotic systems and / or products
- Comfort working in a fast-paced development environment
- Ability to identify unsolved problems and creatively invent solutions
- Proven track record of innovation in robotics and autonomous systems
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