Systems Engineer: Operational Safety

Posted:
8/19/2024, 5:44:18 AM

Location(s):
California, United States ⋅ Foster City, California, United States

Experience Level(s):
Mid Level ⋅ Senior

Field(s):
IT & Security

Workplace Type:
Hybrid

Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities. Zoox’s System Design and Mission Assurance (SDMA) team is responsible for constructing the safety case and validating that our vehicles are safe enough to be deployed for autonomous driving. We play a foundational role for the success of the company. As Zoox prepares to launch our technology on public roads, we are seeking an experienced systems engineer who can bring the system safety engineering discipline to the field operations to ensure safe deployment and continuous operations of Zoox technology. 
Compensation
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. The salary range for this position is $166,000 to $286,000. A sign-on bonus may be offered as part of the compensation package. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
 
Zoox also offers a comprehensive package of benefits including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

Zoox

Website: https://zoox.com/

Headquarter Location: Foster City, California, United States

Employee Count: 1001-5000

Year Founded: 2014

IPO Status: Private

Last Funding Type: Convertible Note

Industries: Autonomous Vehicles ⋅ Machine Learning ⋅ Robotics ⋅ Transportation