We are a stealth startup building perception systems for autonomy. We are based in the 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 (you can see here for more information about us). Our core product grew out of 5+ years of university R&D by our co-founders.
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 for a QA engineer. The candidate will join a multi-disciplinary team of scientists and engineers and support multiple teams across the company.
Responsibilities:
You will be conducting field tests for a product that produces real-time machine learning inference based on input from multiple sensors. These tests include both controlled captures that will be used for internal algorithm development and verification, as well as customer trials. You will also play an integral part across every part of the data pipeline from data collection, to manual and automatic annotation methods, to evaluation, verification, debugging, and root-causing of potential issues. It is expected that you can track and clearly communicate work you are doing related to product readiness, identify any gaps, and consistently assess new and innovative ways to improve the quality and performance of our products.
Some examples of your day to day tasks and requirements:
Engineering Support
Operations Support
Devise and improve processes, including software and procedures, around deployment of product for field tests
Own and run the data pipeline across collection to annotation and post-processing pipelines
On-site debug of software and hardware issues
Support and run field tests, customer installations and demos
Develop and perform signal and performance quality checks to ensure successful deployment
Own and run the data annotation and post-processing pipelines
Basic Qualifications:
General
Preferred Skills:
General