Simulation Engineer

Position: Simulation Engineer Location: Mountainview CA Hybrid Duration: 12+Months with possible extensions Job Description: 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. Key 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. Required Qualifications: 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. Preferred Qualifications: Familiarity with VLA/NOVA model architectures or similar foundation models in robotics. Experience with cloud computing environments (AWS, Azure, Google Cloud Platform) 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.