We have an exciting opportunity on our Software team for a strong leader with exceptional development/research skills in the field of Computer Vision and Machine Learning. The primary responsibility of the Lead Software Researcher / Engineer in Computer Vision is to drive the research and development of core perception components within the agreed upon scope and schedule as defined by the management team. The lead must come with an established expertise in at least one of the following core technical areas of geometric vision and machine learning: sensor calibration (cameras, IMUs, displays), visual inertial odometry, dense environment mapping, eye tracking, 3D scene understanding. The Lead will participate in release planning, scheduling, and assigning individual developers within their technical team. Qualified candidates will be driven self-starters, robust thinkers, strong collaborators, 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.
- 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 the 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.
- Expert knowledge and leadership experience in Computer Vision with 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.
- Visual-Inertial Odometry: Design and implement advanced algorithms for estimating the 6DOF pose of a head-mounted device by optimally fusing visual and inertial measurements collected from multiple cameras and IMUs.
- Large scale mapping: Design and implement advanced algorithms for large scale mapping to extend computer vision spaces to large areas.
- Dense Environment Mapping: Design and implement advanced algorithms for reconstructing dense 3D models of large-scale indoor environments using depth sensors.
- 3D Scene Understanding: Design and implement 3D scene segmentation algorithms based on depth, motion or texture data.
- Eye Tracking: Design and implement advanced algorithms for real-time stereoscopic eye vergence tracking.
- Computer vision algorithms on cloud: Research, architect, and implement high-performance computer vision software in the cloud with state-of-the-art capabilities.
- Expert level in C/C++ (programming and debugging).
- Experience working with OpenCV.
- Experience in Deep Learning is strongly preferred with knowledge of atleast one of TensorFlow, PyTorch, or Caffe.
- Knowledge of parallel computing, OpenCL, CUDA, GPGPU is a plus.
- Knowledge software optimization and embedded programming is a plus
- MS or Ph.D. in Computer Science or Electrical Engineering (with a minimum of 5 years of relevant experience).
- All your information will be kept confidential according to Equal Employment Opportunities guidelines.