About the job
As the Director for Simulation and Evaluation, you will sit at the center of the Global AI Backbone, architecting the multi-level simulation ecosystems required to train, evaluate and validate next-generation Foundation Models. You will bridge the gap between high-fidelity sensor simulation and generative world models, ensuring our AI systems are statistically proven to be safe and robust before hitting the road. This is a global role that requires you to define and manage a global simulation and evaluation ecosystem that enables continuous development and deployment of our ADAS systems.
Responsibilities
Lead AI Simulation & Foundation Model Strategy: Define the roadmap for high throughput, closed-loop simulation.
Research and propose new methodologies to assess the quality, safety, and realism of ML models used for training, evaluation and validation of L2++ and L4 automated driving stacks.
Architect Evaluation Frameworks: Build the infrastructure. Develop tools that allow for rapid iteration of Foundation Models, ensuring model improvements translate into measurable gains in fleet-wide performance.
Drive Generative AI Innovation: Lead the development of World Models to predict and generate complex, realistic edge cases. Build data pipelines for signal discovery, data labeling, and metric computation based on large-scale simulations.
Production Release Authority: Establish the "gold standard" for evaluation that informs SoP (Start of Production) and model release decisions. Translate complex simulation data into technical strategy documentation for executive decision-making.
Global Technical Leadership: Lead, mentor and inspire a cross-functional global team of SWEs, Data Scientists, and ML experts, fostering a culture of rigorous statistical validation and innovative engineering.
Qualifications
Minimum
Master’s or PhD in Computer Science, Electrical Engineering, Machine Learning, Statistics, Physics, or a related quantitative field.
10+ years of experience in software engineering, with a specific focus on embedded systems, automotive, or robotics.
7+ years of experience leading complex software projects from concept to production within the ADAS or Autonomous Driving domain.
Direct, hands-on experience with L2++ or L4 system Start of Production (SoP), specifically overseeing simulation, testing, and validation protocols for high-stakes deployments.
5+ years of involvement with the development or evaluation of large-scale AI, LLMs, or World Models/Generative AI models for simulation and behavioral prediction.
Expert-level understanding of multi-level simulation platforms, including high-fidelity sensor level simulation for perception and scalable object-level simulation for behavioral training.
Proven mastery of data-driven report writing and technical strategy documentation designed for executive decision-making and safety case justifications.
Preferred
5+ years of experience with high-throughput simulation, GPU-accelerated technologies (CUDA), parallel computing, and Reinforcement Learning (RL).
Mastery of C++ and Python. Experience building automated data pipelines for signal discovery, data labeling, and large-scale metric computation.
5+ years of experience managing and mentoring global, multi-disciplinary teams of Software Engineers, Data Scientists, and ML experts.
Working knowledge of automotive industry safety standards and regulations (e.g., ISO 26262, SOTIF) as they apply to virtual validation.