Senior Simulation Engineer

UnitX•Milpitas, CA

About The Position

UnitX builds the world's leading physical AI systems to automate repetitive visual tasks in factories. UnitX is a fast-moving startup with a team from Stanford, MIT, Google, and beyond. Since inception, UnitX has deployed 1,000+ mission-critical systems across 190+ of the world's leading manufacturers' production lines. Every year, $15B worth of products go through UnitX's AI inspection system to ensure quality. Role Overview We are seeking a highly skilled Simulation Engineer to design and build high-fidelity, scalable simulation platforms. In this role, you will architect modular simulation environments that accurately mirror real-world physical interactions and sensor outputs, enabling the robust training, testing, and validation of advanced AI-driven systems. You will play a critical part in closing the sim-to-real gap, ensuring that our simulated environments produce reliable, predictable, and reproducible outcomes in the physical world.

Requirements

Education & Experience: Bachelor’s degree in Computer Science, Robotics, Engineering, or a related field with 4+ years of professional experience; OR a Master’s degree/Ph.D. with 2+ years of relevant experience.
Industry Track Record: Proven experience contributing to the successful release of a customer-facing product or complex system that heavily relies on simulation (e.g., robotic digital twins, autonomous vehicles, UAV systems, or AAA video games).
Technical Proficiency: Strong software engineering fundamentals with deep expertise in C++ or Python.
Simulation Ecosystems: Hands-on experience with modern simulation frameworks (e.g., NVIDIA Isaac Sim, Drake, MuJoCo) or industry-standard 3D game engines (e.g., Unreal Engine, Unity).

Nice To Haves

3D Asset Toolchains: Proficiency with industry-standard 3D asset and robotics description formats (e.g., USD, URDF, MJCF, glTF) and experience building automated CAD-to-sim pipelines.
Machine Learning Integration: Hands-on experience interfacing simulators with modern ML frameworks (e.g., PyTorch, TensorFlow) for synthetic data generation or autonomous agent training.
Advanced Sensor Fidelity: Proven track record of tuning, calibrating, and validating complex simulated sensor suites (e.g., optical, depth, spatial, or tactile sensors) against physical counterparts.
Distributed Computing: Familiarity with cloud platforms (AWS, GCP) and containerization (Docker, Kubernetes) for parallelizing and scaling massive simulation workloads.

Responsibilities

Platform Architecture: Design, develop, and maintain a modular, extensible, and customizable simulation platform tailored for scalable AI applications.
Physics & Dynamics Modeling: Leverage state-of-the-art simulation and physics engines to accurately model realistic physical interactions, including rigid body dynamics, complex geometries, and diverse material properties.
Sensor Simulation & Calibration: Develop and calibrate high-fidelity simulation models for diverse sensor modalities. Establish rigorous, quantitative validation metrics to ensure simulated perception accurately matches real-world ground truth.
ML & Synthetic Data Pipelines: Architect scalable pipelines for high-throughput offline synthetic data generation, ensuring seamless integration with machine learning training loops (e.g., Reinforcement Learning, Computer Vision).
Sim-to-Real Optimization: Proactively identify and mitigate risks, failure modes, and bottlenecks for synthetic data generation pipeline. Define and monitor metrics to continually measure and minimize the sim-to-real gap.
Hardware & Environment Modularization: Implement highly configurable asset pipelines to support a wide variety of hardware topologies, sensor configurations, and dynamic operational layouts.
Scaling Future Simulation Diversity: Architect foundational systems capable of supporting next-generation, contact-rich interactions. You will lay the groundwork for our future efforts in advanced domain randomization—expanding spatial, visual, and physical parameters—as our AI models scale in complexity over time.