[robotics-worldwide] [jobs] Perception Lead position at nuTonomy in Boston
nuTonomy (www.nutonomy.com) is focused on research and development of
state-of-the-art software for self-driving cars. Founded by Karl Iagnemma
(former Director of Robotic Mobility Group, MIT) and Emilio Frazzoli
(former Professor of Aeronautics and Astronautics, MIT), we aim to be the
first company in the world to launch an autonomous taxi system. We are
building up an awesome team in Boston, Santa Monica and Singapore to make
this goal a reality. Recently, we launched the world's first public trial
of autonomous vehicles in Singapore, and are also testing on public roads
We are seeking a Perception Lead to guide nuTonomy’s growing perception
team, which is developing a wide range of perception capabilities,
including: multimodal sensor fusion (lidar, camera, radar, …), SLAM/SfM,
object detection, object tracking, scene understanding, automatic sensor
calibration and fault detection, and all other capabilities necessary for
making a fleet of vehicles autonomous. nuTonomy’s strategy is heavily
focused on a holistic approach to the problem, using task-driven perception
and joint inference and control.
The perception lead will be responsible for leading the overall design of
the perception system, as well as its integration in the larger system.
They will have the responsibility of identifying goals/milestones, writing
technical roadmaps, and the determination of performance metrics and
Ph.D. in Computer Science, Electrical Engineering, Aerospace, Controls,
or a related field, with respectable publication record in at least one of
the core fields mentioned above.
Experience recruiting, building & leading large engineering teams.
Experience developing large software projects.
Excellent communication and writing skills.
Experience in the automotive industry, especially in ADAS systems.
Experience in the aerospace industry, especially in safety-critical
Experience with building autonomous systems containing both perception
Deep understanding of estimation theory and filtering theory (i.e. being
intimately familiar with terms such as sigma-algebra, minimal sufficient
Working knowledge of modern machine learning methods.
Working knowledge of planning and control theory.
Knowledge of formal methods of systems engineering and software