Zoox is on an ambitious journey to develop a full-stack autonomous vehicle system for cities. We are seeking a Staff Data Scientist to join a verification and validation team that evaluates safety-critical AI systems.
You will join a team of software and data engineers that leverage methods including log data analysis, simulation, and closed-course structured testing. You'll work cross-functionally with AI software, System Design and Mission Assurance, Simulation, Sensors, and other teams to develop, execute, and iterate on validation methods and pipelines. These pipelines evaluate safety-critical systems, are highly visible, and are an important critical path element of launching our service. The ideal candidate brings a hybrid of statistical rigor and engineering mindset to drive clarity from ambiguity, establish new processes, and propel the team forward. This is a deeply technical and hands-on role where you will be expected to be a self-sufficient builder and coder, not just a manager of projects.
In this role, you will:- Design Evaluation Frameworks: Architect statistical methodologies for safety-critical AI systems to form objective, rigorous conclusions about their performance and reliability.
>- Conduct Robust Analysis: Deliver validation evidence to support increasingly complex operations and identify potential edge-case failures.
>- Inform Strategy: Deliver clear, data-driven insights to development teams to guide system improvement, and to executive leadership to inform milestone-level go/no-go decisions.
>- Define Metrics: Drive alignment across engineering teams on performance metrics and data extraction strategies.
>- Lead the Lifecycle: Manage all phases of evaluation including prototyping, requirements capture, design, implementation, and validation.
>- Scale Pipelines: Partner with engineers to build and maintain scalable data processing and simulation pipelines, applying distributed computing to analyze petabytes of driving data.
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Qualifications:- MS or PhD in Statistics, Computer Science, Machine Learning, Applied Mathematics, or related quantitative field
- Proficiency in Python and SQL with experience in production-quality code
- Demonstrated expertise in statistical methodologies including hypothesis testing, power analysis, spatiotemporal modeling, Bayesian inference, and multivariate analysis.
- Experience with large-scale data analysis and statistical modeling
- Proficiency with Git, unit testing, and collaborative development practices
Bonus Qualifications:- Hands-on experience with production machine learning pipelines: dataset creation, training frameworks, metrics pipelines
>- Experience with modern data processing technologies such as Apache Spark, Spark SQL, and Databricks
>- Experience with designing metrics and delivering actionable insights that drive business decisions
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$256,000 - $307,000 a year
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. 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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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