Software Simulation Engineer, Sensor Rendering

At Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.

Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.

We're looking for a Software Simulation Engineer to help us stand up and scale our sensor simulation infrastructure for sim-to-real model training. You will own the rendering and simulation of our 2D and 3D sensors, which our perception models rely on. You'll produce photorealistic, physically accurate synthetic data, enabling us to train and validate perception systems faster and at a greater scale than real-world data alone allows.

What You'll Do

Experienced Level:

• Implement and validate physics-based sensor simulation models (structured light, depth, RGB, stereo, etc.) within platforms such as NVIDIA Isaac Sim, Blender or Unreal Engine, producing outputs that closely match real sensor behavior.
• Build photorealistic scene rendering pipelines that account for sensor placement on the robot end-effector. Emulate robot trajectories both with and without physical models. Utilize accurate material properties, such as metal reflectance, weld spatter, and torch glow, to ensure synthetic data is meaningful for perception model training.
• Develop synthetic data generation pipelines producing annotated ground-truth (point clouds, depth maps, segmentation masks) at scale.
• Implement domain randomization strategies (lighting, material variation, sensor noise, viewpoint perturbation) to improve sim-to-real transfer for downstream perception models.
• Collaborate with perception teams to ensure rendered outputs meet dataset requirements and write high-quality Python code.

Senior Level:

• Lead the design and validation of high-fidelity, photorealistic sensor render pipelines grounded in real sensor characterization data and validated against physical measurements.
• Architect the sensor rendering strategy for the Perception team, defining which sensor modalities, material models, and environmental conditions must be simulated to support perception across the full weld cell workflow.
• Own the sim-to-real validation framework: define quantitative benchmarks and go/no-go criteria for when synthetic sensor data is ready to feed production model training.
• Drive 3D asset and environment pipeline strategy, including CAD-to-simulation workflows, SDF/URDF asset management, material library management, and procedural scene generation for weld cell environments across Gazebo, Isaac Sim, and Unreal Engine.
• Define strategy for when and how to use each simulation platform (Gazebo for ROS-integrated functional testing, Isaac Sim or Unreal Engine for photorealistic synthetic data generation) and build workflows that span them coherently.
• Mentor engineers on rendering best practices, physically based material modeling, Gazebo plugin development, and synthetic data methodology.

• Education & Experience: Degree in CS/Robotics/EE plus 3+ years (Experienced) or 5+ years (Senior) in simulation, rendering, or perception.
• Software Proficiency: Strong Python skills for building production-grade simulation tooling and plugins.
• Simulation Platforms: Hands-on experience with NVIDIA Isaac Sim, Unreal Engine, Blender or Gazebo (Classic/Ignition).
• Rendering & Assets: Solid understanding of Physically Based Rendering (PBR) and experience with 3D assets (URDF, SDF, USD).
• 3D data and assets: Experience with mesh representations, material authoring, and CAD-to-render workflows using formats such as URDF, SDF, or USD.
• Synthetic data pipelines: Experience building annotated synthetic dataset generation systems and domain randomization strategies aimed at real-world model training.
• Generative AI: Experience with generative image AI (e.g., diffusion models) and its application in synthetic data generation.

• Direct experience with NVIDIA Omniverse / Isaac Sim and USD-based scene composition.
• Familiarity with simulating industrial phenomena like arc flash, weld spatter, and thermal emission.
• Experience with generative AI (diffusion models) and procedural geometry for scalable 3D mesh generation.
• Prior work in manufacturing or automotive simulation and cloud-based render farm infrastructure.
• Experience with GPU-accelerated rendering or cloud-based render farm infrastructure.

• Free lunch every day
• Flexible PTO
• Medical, Dental, and Vision insurance
• 6 weeks 100% paid parental leave plus an additional 6-8 weeks maternity leave for the birthing parent (12-14 weeks total)
• 401K through Empower
• Paid Referral Bonus