We are seeking a highly skilled Senior Simulation Engineer to own and accelerate our synthetic data generation capabilities. This role is crucial for bridging the gap between our high-fidelity simulation environment and our production ML models. You will be responsible for architecting, implementing, and maintaining the entire simulation-to-data pipeline, ensuring a consistent and massive flow of quality training data. This will be an on-site position, working alongside our client in Mountain View, CA. Req#: [redacted] Responsibilities Simulation Acceleration & Data Collection: Directly utilize expertise in NVIDIA Isaac Sim to design, script, and optimize simulation scenarios specifically tailored to generate high-quality, diverse data for VLA (Vision-Language Alignment) and NOVA model training objectives Pipeline Architecture: Design, build, and maintain robust, scalable pipelines for CAD ingestion, scene creation, sensor emulation, and data processing within the Isaac Sim framework Synthetic Data Management: Implement and manage advanced auto-annotation tools within the simulation environment to rapidly label complex sensory data (e.g., 3D bounding boxes, semantic segmentation, depth maps) for supervised learning Environment Maintenance: Take ownership of setting up, configuring, and maintaining the simulation environment (including hardware/software dependencies, GPU utilization, and headless operation) to ensure reliable, large-scale parallel execution Cross-Functional Support: Collaborate closely with the Machine Learning Engineering team to analyze data gaps, iterate on simulation parameters, and ensure the synthetic data distribution matches the necessary complexity and variability of real-world deployment Performance Tuning: Profile and optimize simulation execution speed to maximize data throughput, essential for rapid iteration cycles in model tuning Requirements Expertise in Simulation: 3+ years of hands-on experience with NVIDIA Isaac Sim (or a comparable high-fidelity physics simulator like Unity/Unreal used for robotics) Programming Proficiency: Expert-level proficiency in Python for scripting, automation, data pipeline construction, and tool development Robotics Fundamentals: Strong understanding of robotics kinematics, sensor physics (LIDAR, RGB-D cameras, IMU), and their accurate representation in simulation ML Data Pipeline Experience: Proven experience setting up automated data collection pipelines for Deep Learning projects, including experience with data versioning and metadata management CAD/3D Workflow: Familiarity with 3D assets, CAD formats, and procedural generation techniques to create complex virtual scenes Familiarity with VLA/NOVA model architectures or similar foundation models in robotics Experience with cloud computing environments (AWS, Azure, GCP) for scaling simulation jobs Familiarity with other robotics simulation/middleware like ROS/ROS 2 Experience with hardware-in-the-loop (HIL) or software-in-the-loop (SITL) testing methodologies