Quantitative Risk Intern

Number of employees

4100

Foster City, CA, United States

Posted on: 2024-11-26

Category: emobility

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Expired

Employment type:

Internship

Experience required:

Intern

Salary

Salary not provided

About the company:

Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.


Zoox is looking for a Safety Research Engineer candidate with a strong statistics background to join the Quantitative Risk Assessment (QRA) team. In this role, you will be working with cross-functional stakeholders in the autonomy and platform teams to design, implement, and validate off-vehicle performance metrics that will be used to inform our readiness to deploy autonomous vehicles on public roads. 

Compensation
The monthly salary range for this position is $6,500 to $9,500. Compensation will vary based on role, degree level and type, and benefits will be offered based on eligibility. Additional benefits may include medical insurance, 401k, and a housing stipend.

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.


Accommodations
If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.

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.

Technical Qualifications & Language Requirements

  • Pursuing a Master’s or PhD Degree in Math, Statistics, Computer Science, or other related technical field
  • Experience or familiarity with analysis of large datasets. Bonus: familiarity with extracting sparse signals from large datasets.
  • Solid understanding of statistical tools, such as importance sampling and Bayesian models
  • Familiarity with Python, Scala, and/or SQL
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