Lead Perception Engineer

ppGravis Robotics is a startup that turns heavy construction machines into intelligent and autonomous robots. Our unique combination of learning-based automation and augmented remote control lets one operator safely conduct a fleet of earthmoving machines in a gamified environment. Our team has over a decade of academic experience honing the cutting edge of large-scale robotics, and is rapidly growing to bring that expertise into a trillion dollar industry through active deployments with market leaders. /p pbAbout the Job /b /p pAt Gravis, we engineer solutions at the nexus of hardware and software every day: bringing new perception and control technologies onto awesome, autonomous machines. Our Rooftop Autonomous Control Kit (Rack) combines sensors, compute, communication and networking modules toward a manufacturer-agnostic solution that can be applied to a variety of construction machines regardless of type and age. We are seeking a highly skilled and experienced lead for our perception team: you will spearhead the perception development process, from design and prototyping to deployment of state-of-the-art object detection, scene segmentation, mapping, state estimation and calibration algorithms-while ensuring production quality implementation and timely execution. /p br/ h3What you’ll do /h3 ul liManage the design, prototyping, and deployment of advanced real-time perception and deep learning algorithms for autonomous heavy machines, focusing on 3D object detection tracking, semantic segmentation, visual/lidar odometry, mapping, and sensor calibration. /li liLead the perception team and the agile development process, working with leadership to set and maintain project timelines. /li liEstablish and optimize testing procedures and performance metrics to ensure robust system behavior. /li liOversee the integration of new sensors and configurations into the perception stack. /li liCollaborate closely with multidisciplinary experts to improve the reliability and performance of the entire system. /li liPropose and manage hiring initiatives to strategically build a nimble and highly effective perception team. /li /ul h3What you’ll bring /h3 ul liMaster’s or PhD in Computer Science, Mechanical Engineering, Electrical Engineering or a related field. /li li5+ years of experience in developing and implementing robust perception algorithms. /li liWriting production-quality C++/Python code in a Linux development environment. /li liExtensive experience with deep learning frameworks (pytorch, tensorflow, etc) using image and/or LiDAR data. /li liProven leadership in a collaborative, small-team environment, with a strong ability to drive projects across disciplines. /li liExcellent project management skills with the ability to prioritize tasks, manage resources, and meet deadlines. /li liExcellent communication skills with the ability to effectively convey technical concepts to both technical and non-technical stakeholders. /li /ul h3Additional beneficial skills /h3 ul liExperienced with setting up and maintaining machine learning pipelines, from data collection to model deployment /li liProficiency with common robotics perception frameworks ( e.g. OpenCV, PCL, Open3D, ROS 2, Nvidia HW/SW ecosystem, etc.) /li /ul br/ pThis is an opportunity to join a dynamic and versatile team, and to be part of a young startup that will revolutionize heavy construction. /p pAs a forward-facing startup, we understand that work-life balance and flexibility are important considerations for many professionals. /p pGravis is an equal opportunity employer. We are committed to building an inclusive and diverse team, and do not discriminate based upon race, color, ancestry, national origin, religion, sex, sexual orientation, age, gender identity, gender expression, disability, veteran status, or other legally protected characteristics. /p pWe are an international team that is working to solve problems with a global impact: to facilitate efficient communication and collaboration, proficiency in English is a requirement for all roles. /p /p #J-18808-Ljbffr