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 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 an exciting opportunity in the area of signal processing in San Carlos, CA.
You will join a multi-disciplinary group of scientists and engineers and will have the opportunity to collaborate with cross-functional product and research teams. An ideal candidate will have a proven track record of driving zero-to-one innovation for large-scale system projects. This position is open to both on-site and remote candidates (including Canada)
Responsibilities
- Develop signal processing algorithms, including detection, estimation, prediction, and tracking, to interpret FMCW Radar data.
- Prototype, benchmark, and test new radar signal processing algorithms.
- Identify required evaluation datasets and plan/manage data captures.
- Develop simulation models for interpreting experimental Radar data.
- Maintain and improve Radar signal processing algorithms and models, including continuous evaluation, gap analysis, re-training, and fine tuning.
- Own the tuning of the algorithms to meet performance requirements including recall and precision.
- Support the product development cycle and subsequent verification phases including performance evaluation efforts.
Basic Qualifications
- A graduate degree in engineering (MSc or PhD) in following fields and/or work experience in:
- Waveform design for active sensing solutions (e.g. radar waveform design)
- Theory and algorithms for signal detection and estimation
- Algorithm development for sensing and perception (especially related to radar pipelines), e.g. image reconstruction, object detection, and parameter estimation
- Localization and tracking algorithms, including Kalman and particle filters
- ADAS radar architectures and signal processing schemes
- FFT based processing, MUSIC algorithm, filtering, clustering and feature extraction
- Understanding of Radar operation principles and the related theoretical framework.
- Experience with fixed-point arithmetic and numerical methods.
- Working experience with MATLAB and/or Python, and familiarity with C++.
- Self motivated with excellent problem solving and technical communication skills.
Preferred Qualifications
- Prior experience developing sensor fusion algorithms using data from different sensing modalities including Radar, Camera, and ToF/LiDAR systems.
- 3 to 5 years of experience with product level development and support.
Cultural Fit
- Ability to work in a small and fast-paced environment.
- A positive attitude, team-oriented spirit, and enthusiasm for our goals.
- A curious outlook - willingness to learn new things and solutions to fit the problem.
- A strong sense of ownership and passion for completing a job well - attention to detail.
- Ability to work well with all levels of company regardless of seniority.
- Sense of humor and ability to proactively problem solve.
