You will:- Calibrate and validate physical reachability and safety-margin metrics against expert human evaluations to quantify "proactive caution" in complex scenarios
- Analyze large-scale real-world and simulated driving logs to evaluate model accuracy, characterize edge cases, and optimize trade-offs
- Partner cross-functionally to integrate quantitative safety signals into automated event triage and release-readiness workflows
You have:- Currently pursuing an Master's or PhD degree in Computer Science, Engineering, Physics, Statistics, Data Science, or a related quantitative field
- Proficiency in Python and/or C++
- Experience analyzing, wrangling, and visualizing large-scale datasets
- Demonstrated analytical and statistical skills for validating quantitative models
We prefer:- Familiarity with SQL
- Familiarity with autonomous vehicles, robotics, crash safety analysis, or human driving behavior modeling
- Experience working with rare-event modeling, anomaly detection, or highly imbalanced datasets in complex real-world domains (e.g., autonomous systems, robotics, experimental physics, crash safety, or healthcare)
- Solid foundation in machine learning and statistical evaluation fundamentals
Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.
The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company's generous benefits programs, subject to eligibility requirements.
Hourly Masters Pay
$70-$70 USD
The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company's generous benefits programs, subject to eligibility requirements.
Hourly PhD Pay
$85-$85 USD
