Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.
Our group's mission is to design, prototype and productionize novel methods and tools for efficient modeling and simulations of complex systems at scale. These tools aid in measuring the safety of our AI and readiness for launch. We use recent advances in machine learning, statistics, optimization, and numerical methods to verify and validate our software and AI in the most challenging environments, and subsequently develop tools to enable other teams at Zoox to do the same.
We are looking for software engineers to help improve autonomous driving safety using large-scale, distributed optimization and machine learning. In this role, you will leverage simulation and real-world driving data to design efficient solutions leveraging cutting-edge rigorous methods. You will work cross-functionally with engineers in AI, simulation, infra, and data science to push the boundaries of scalability and performance, and bring state-of-the-art machine learning and optimization applications to production.
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 $160,000 to $226,000. 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.
Vaccine Mandate
Employees working in this position will be required to have received a single dose of the J&J/Janssen COVID-19 vaccine OR have completed the two-dose Pfizer or Moderna vaccine series. In addition, employees will be required to receive a COVID-19 booster vaccine within two months of becoming eligible for the booster vaccine.
Employees will be required to show proof of vaccination status upon receipt of a conditional offer of employment. That offer of employment will be conditioned upon, among other things, an Applicant’s ability to show proof of vaccination status. Please note the Company provides reasonable accommodations in accordance with applicable state, federal, and local laws.
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
Responsibilities
Design and implement algorithms and methods to improve optimization capabilitiesDesign large-scale optimization problems in an efficient wayAnalyze driving data and identify novel approachesLead complex cross-functional projectsCommunicate your work to other teams at ZooxQualifications
MS or PhD in optimization, statistics, or machine learningSolid computer science fundamentalsFluency in PythonExperience with numerical optimizationExperience with distributed systems and running services in the cloudBonus Qualifications
Experience with black-box optimization and its application to hyperparameter tuning or large-scale simulationsExperience with multi-objective optimizationExperience with autonomous vehiclesProficiency with Python's scientific stack, incl. machine learning and statistical frameworks (numpy, scipy, pandas, pytorch, pyMC3, etc.), and practical experience with distributed & GPU computing and modern SW development practices.