About
We are a series-A startup building perception systems for autonomy. We are based in the San Francisco Bay Area, funded by NEA, and our core team includes faculty entrepreneurs (Stanford and UC Santa Barbara) and industry veterans (Uber, Apple, Amazon Lab126, Rohde & Schwarz), who have successfully shepherded signal processing and machine learning innovations to large-scale software for location improvement and safety at Uber, led the development of state-of-the-art computer vision technologies that shipped over millions of Amazon devices, and delivered zero-to-one product experiences at Uber and Box. Our core product grew out of 5+ years of university R&D by our co-founders. You can find out more about us by visiting our website.
Our mission and team expertise spans beyond software to advanced sensor systems, algorithms, embedded systems, signal processing, and machine learning. Our team is building and deploying edge software and cloud services for real-time customer facing products as well as internal big data tools. We look for people with a depth of expertise and experience in one of these areas, and with the intellectual curiosity for interacting with, learning from, and teaching world-class experts in areas outside their expertise.
We currently have multiple internship openings in the areas of computer vision and machine learning. The candidates will join a multi-disciplinary team of scientists and engineers and work on a full stack of developing cutting edge Computer Vision (CV) and Machine Learning (ML) methods based on data from a variety of sensor modalities. This position is open to both on-site and remote candidates (including Canada)
Responsibilities
Basic Qualifications
Preferred Qualifications
Prior experience with multi-sensor calibration and multi-view geometry
Hands-on experience with different neural network architectures (CNNs, RNNs, etc.) as well as specific approaches for classification, segmentation, and object detection (Mask-RCNN, SSDs, EfficientDet, …) and common datasets (CoCo, Kitti, nuScenes,...)
Solid software engineering foundation and a commitment to writing clean, well architected code
Familiarity with various physical aspects of sensors including cameras and Lidars
Publications in major CV/ML conferences and journals
Statistical modeling, analysis, and significance testing
Experience with edge computing (NVidia Jetson family, Raspberry Pi, ML accelerators) and coding for resource-constrained compute environments