Director, Simulation and Evaluation - Autonomous Driving

Bosch Group•Sunnyvale, CA

5d•Hybrid

About The Position

Bosch is seeking a Director for Simulation and Evaluation to lead a global simulation and evaluation ecosystem supporting continuous development and deployment of autonomous driving and ADAS systems. The role sits at the center of the Global AI Backbone and will architect multi-level simulation ecosystems for training, evaluating, and validating next-generation Foundation Models. It bridges high-fidelity sensor simulation and generative world models to help establish statistical evidence that AI systems are safe and robust before road deployment. The Director will define the strategy and roadmap for high-throughput, closed-loop simulation; develop evaluation frameworks and infrastructure for rapid Foundation Model iteration; and lead World Model development to predict and generate realistic edge cases. The position also establishes evaluation standards for Start of Production and model-release decisions, turns simulation results into executive-level technical strategy documentation, and leads a global, cross-functional team of software engineers, data scientists, and machine-learning experts.

Requirements

Master’s or PhD in Computer Science, Electrical Engineering, Machine Learning, Statistics, Physics, or a related quantitative field.
At least 10 years of software-engineering experience, focused on embedded systems, automotive, or robotics.
At least 7 years leading complex software projects from concept to production in the ADAS or Autonomous Driving domain.
Hands-on experience with L2++ or L4 system Start of Production, including oversight of simulation, testing, and validation protocols for high-stakes deployments.
At least 5 years involved in developing or evaluating 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.
Demonstrated mastery of data-driven report writing and technical strategy documentation for executive decision-making and safety-case justifications.

Nice To Haves

At least 5 years of experience with high-throughput simulation, GPU-accelerated technologies such as CUDA, parallel computing, and Reinforcement Learning.
Mastery of C++ and Python.
Experience building automated data pipelines for signal discovery, data labeling, and large-scale metric computation.
At least 5 years managing and mentoring global, multidisciplinary teams of software engineers, data scientists, and machine-learning experts.
Working knowledge of automotive safety standards and regulations, such as ISO 26262 and SOTIF, as they apply to virtual validation.

Responsibilities

Define the strategy and roadmap for high-throughput, closed-loop AI simulation.
Research and propose methodologies to assess the quality, safety, and realism of machine-learning models used to train, evaluate, and validate L2++ and L4 automated-driving stacks.
Architect evaluation frameworks and infrastructure that support rapid Foundation Model iteration and measure improvements in fleet-wide performance.
Lead the development of World Models that predict and generate complex, realistic edge cases.
Build data pipelines for signal discovery, data labeling, and metric computation based on large-scale simulations.
Establish evaluation standards that inform Start of Production and model-release decisions.
Translate simulation data into technical strategy documentation for executive decision-making.
Lead, mentor, and inspire a global cross-functional team of software engineers, data scientists, and machine-learning experts.
Foster rigorous statistical validation and innovative engineering.