Lead, Computer Vision Researcher/Engineer, Hand Tracking
Sunnyvale, CA; Toronto, ON; Zurich, Switzerland
We have an exciting opportunity on our Perception team for a strong leader with exceptional development/research skills in the field of Computer Vision and Machine Learning. The primary responsibility of the role is to lead the research and development of multiple core components for hand pose/shape estimation and gesture recognition. The candidate’s responsibilities extend to working closely with the executive team to establish the scope and schedule of the product critical projects, drive the formation of technical teams and ensure a cohesive alignment of all essential technical expertises by setting optimal communication strategies. The lead researcher/engineer must come with a world recognized expertise in at least one of the following core technical areas of geometric vision and machine learning: hand detection, hand pose estimation, standard parameterized 3D hand shape model and hand gesture recognition. Qualified candidates will be driven self-starters, robust thinkers, strong collaborators, effective leaders and adept at operating in a highly dynamic environment. We look for colleagues that are passionate about our product and embody our values.
- Provide leadership and mentoring to the research and development team within the Computer Vision group.
- Lead the research and development effort of advanced product-critical computer vision components covering key product critical perception features such as head pose tracking, eye tracking, environment mapping, sensor calibration.
- Define and execute the roadmap of new features.
- Actively contribute to Magic Leap Intellectual Property and publish the research findings in peer-reviewed conferences.
- Work hand-in-hand with the key stakeholders and developers across the company using computer vision components.
- Support overall research engineering and architecture efforts of computer vision and machine learning components.
- Write maintainable, reusable code, leveraging test driven principles to develop high quality geometric vision and machine learning modules.
- Troubleshoot and resolve software defects and other technical issues.
- Act as a mentor and subject matter expert within the computer vision group and with other key stakeholders.
- Review individual developer's code in the team to ensure highest code quality in Computer Vision components.
- 7+ years of working experience in Computer Vision targeted to product development.
- Experience leading engineering teams from first concept to ship.
- World recognized expert knowledge and strong leadership experience in Computer Vision with extensive publication record and specialization in at least one of the following domains:
- Sensor Calibration: Design and implement algorithms for online and offline intrinsic and extrinsic calibration of complex devices composed of several sensors, cameras, IMUs, depth sensors, and imagers. Collaborate with other engineers on the design and deployment of fully automatic robotics-aided calibration processes targeted for factory production.
- Hand Pose Estimation and Tracking: Research, architect and hands-on experience on the hand or body pose estimation and tracking. Be familiar with the standard parameterized 3D hand or body shape model.
- Computer vision algorithms on device: Research, architect, and implement high-performance computer vision software on device with state-of-the-art capabilities.
- Expert level in C/C++ (programming and debugging).
- Experience working with OpenCV.
- Experience in Deep Learning with knowledge of at least one of TensorFlow or PyTorch.
- Knowledge of parallel computing, OpenCL, CUDA, GPGPU is a plus.
- Knowledge of software optimization and embedded programming is a plus.
- MS in Computer Science or Electrical Engineering (with a minimum of 7 years of relevant experience).
- Ph.D. is preferred (with a minimum of 5 years of relevant experience).
- All your information will be kept confidential according to Equal Employment Opportunities guidelines.
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