PlusAI
Physical AI
Simulation/MLEngineerIntern
Neural analysis suggests this role is
optimal for Entry candidates.
“Simulation/ML Engineer Intern at PlusAI. Skills: Large Language Models (LLMs), Simulation technology, Machine learning, Robotics, Generative AI. Develop a tool that can generate realistic, scalable simulation scenarios from text and real road data. Leverage Large Language Models (LLMs) to architect and implement a pipeline that automatically generates realistic, highly structured driving scenarios”
What You'll Achieve.
Generate realistic, scalable simulation scenarios from text and real road data; Create executable, varied testing grounds; Ensure generated scenarios strictly adhere to or properly test complex traffic laws, rights-of-way, and safety protocols; Refine models as needed for realism, diversity, and edge-case coverage
Industry & Context.
Tackling complex challenges at the intersection of AI and physical-world simulation
What They're Looking For.
Must Have
Strong, hands-on programming skills in Python for machine learning workflows and scripting, Ability to translate abstract, real-world concepts (like traffic rules and driving environments) into structured, programmable data and logic, A highly analytical mindset with a passion for tackling complex challenges at the intersection of AI and physical-world simulation, understanding of Large Language Models, including prompt engineering, API integration, and structuring LLM outputs (e. g. , JSON parsing)
Nice to Have
Familiarity with autonomous vehicle simulation platforms (e. g. , CARLA, LGSVL, or proprietary AV simulators) and procedural generation, Knowledge of autonomous driving concepts, Operational Design Domains (ODDs), ADAS systems, or NHTSA safety guidelines, Exposure to formal logic, deontic logic, or building rule-based expert systems, Prior experience using generative AI for synthetic data generation, automated testing, or scenario creation
What You'll Do.
Develop a tool that can generate realistic
scalable simulation scenarios from text and real road data
Leverage Large Language Models (LLMs) to architect and implement a pipeline that automatically generates realistic
highly structured driving scenarios
Convert complex Operational Design Domains (ODDs) and safety/NHTSA guidelines into effective prompt frameworks and programmatic constraints for the model
Interface the LLM-generated scenarios directly with the autonomous vehicle simulation environment to create executable
varied testing grounds
Incorporate rule-based constraints (deontic logic) to ensure the generated scenarios strictly adhere to or properly test complex traffic laws
and edge-case coverage of the synthetic scenarios
refining the models as needed
How You'll Work.
Team & Collaboration
Collaborate with the simulation and machine learning teams to evaluate the realism, diversity, and edge-case coverage of the synthetic scenarios
Full Job Description
## Description PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams. We’re seeking an enthusiastic and driven Simulation/ML Engineer Intern to join our team and work on an exciting project that blends Large Language Models (LLMs) with simulation technology. In this role, you’ll help develop a tool that can generate realistic, scalable simulation scenarios from text and real road data. This is a fantastic opportunity to apply your machine learning and robotics knowledge to real-world challenges, while working on a project that will revolutionize how simulation scenarios are created, with minimal manual effort. You’ll be at the forefront of innovation, helping us expand our capabilities in testing autonomous vehicles using large-scale simulation with LLM-driven solutions. ## 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 scena
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