Summary
Careerflow.ai is seeking a highly analytical and detail-oriented Driving Scene Quality Analyst. The role involves reviewing autonomous vehicle sensor feeds, evaluating AI-generated reasoning, and ensuring accuracy and clarity in outputs while applying U.S. traffic laws and driving logic.
Responsibilities
- Review 30-second autonomous vehicle sensor feeds and interpret driving scenes from a first-person perspective
- Evaluate and edit LLM-generated thinking traces for logical consistency and clarity
- Ground entities and events to scene coordinates and object references
- Revise driving plans to reflect correct actions, justifications, maneuvers, and speed
- Identify and remove hallucinated objects or events from AI-generated text
- Distinguish between actual sensor data and fabricated model outputs
- Maintain a zero-error standard in safety-critical judgments under time pressure
- Validate that outputs align with observed trajectory, maneuver, and speed
- Apply U.S. traffic laws and driving logic to evaluate scenarios
Skills
- Experience in roles requiring precise writing and structured analytical thinking
- Documented driving experience in the U.S. with strong knowledge of traffic laws
- Professional fluency in English with high clarity and precision
- Ability to evaluate logical coherence in multi-step reasoning
- Strong attention to detail and ability to maintain quality under time constraints
- Familiarity with LLM-generated content and common failure modes
- Understanding of hallucinations, reasoning inconsistencies, and instruction-following errors in AI systems
Qualifications
Must Haves
- Experience in roles requiring precise writing and structured analytical thinking
- Documented driving experience in the U.S. with strong knowledge of traffic laws
- Professional fluency in English with high clarity and precision
- Ability to evaluate logical coherence in multi-step reasoning
- Strong attention to detail and ability to maintain quality under time constraints
Nice to Haves
- Familiarity with LLM-generated content and common failure modes
- Understanding of hallucinations, reasoning inconsistencies, and instruction-following errors in AI systems