Simulation Engineer Intern

Salary Range: 19 to 65 (Currency: USD) (Pay period: per-hour-wage)

Responsibilities:

• LLM Integration: Leverage Large Language Models (LLMs) to architect and implement a pipeline that automatically generates realistic, highly structured driving scenarios.
• Translate ODDs to Prompts: Convert complex Operational Design Domains (ODDs) and safety/NHTSA guidelines into effective prompt frameworks and programmatic constraints for the model.
• Simulation Integration: Interface the LLM-generated scenarios directly with the autonomous vehicle simulation environment to create executable, varied testing grounds.
• Apply Deontic Logic: Incorporate rule-based constraints (deontic logic) to ensure the generated scenarios strictly adhere to or properly test complex traffic laws, rights-of-way, and safety protocols.
• Quality Assurance & Iteration: Collaborate with the simulation and machine learning teams to evaluate the realism, diversity, and edge-case coverage of the synthetic scenarios, refining the models as needed.

Required Skills:

• Generative AI & LLM Experience: Strong understanding of Large Language Models, including prompt engineering, API integration, and structuring LLM outputs (e.g., JSON parsing).
• Programming Proficiency: Strong, hands-on programming skills in Python for machine learning workflows and scripting.
• Systems Thinking: Ability to translate abstract, real-world concepts (like traffic rules and driving environments) into structured, programmable data and logic.
• Problem Solving: A highly analytical mindset with a passion for tackling complex challenges at the intersection of AI and physical-world simulation.

Preferred Skills:

• Simulation Software: Familiarity with autonomous vehicle simulation platforms (e.g., CARLA, LGSVL, or proprietary AV simulators) and procedural generation.
• Autonomous Driving Context: Knowledge of autonomous driving concepts, Operational Design Domains (ODDs), ADAS systems, or NHTSA safety guidelines.
• Formal Logic/Rules Engines: Exposure to formal logic, deontic logic, or building rule-based expert systems.
• Synthetic Data Generation: Prior experience using generative AI for synthetic data generation, automated testing, or scenario creation.