Bedrock Robotics
2027 Internship Simulation Engineer, Neural Rendering
About this role
Join Bedrock's simulation team as an intern to develop neural rendering techniques that create photorealistic construction environments for training autonomous excavators. You'll research and integrate advanced neural scene representations into their simulation pipeline while measuring how visual improvements translate to real-world autonomy performance.
What you'll do
- Research and implement neural rendering methods (NeRF, 3D Gaussian splatting) for construction-site imagery generation
- Train models on real fleet data and evaluate visual fidelity, temporal consistency, and rendering speed
- Integrate neural rendering outputs into the simulation pipeline for perception and planning teams
- Build evaluation metrics to quantify how rendered scenes match real data and impact autonomy performance
- Identify and address construction-specific challenges like dynamic objects, deformable terrain, and harsh lighting
- Document findings and provide recommendations on neural rendering deployment in the simulation stack
What they're looking for
- Python and PyTorch
- 3D graphics fundamentals (rendering pipelines, scene representations, cameras)
- Neural scene representations (NeRF, Gaussian splatting, neural radiance fields)
- Real-world data handling and processing
- C++ and GPU programming (CUDA, OpenGL, Vulkan)
- Simulation engines (Unreal, Unity, NVIDIA Isaac Sim)
- Novel view synthesis or differentiable rendering
- Autonomous vehicle or synthetic data generation concepts
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Bedrock Robotics
Bedrock Robotics develops autonomous construction machinery powered by AI and robotics technology. The company is hiring for roles spanning developer infrastructure, simulation systems, hardware engineering, field robotics application, and frontend engineering to support the development and deployment of autonomous excavators and heavy equipment.
View all jobs at Bedrock RoboticsLikely interview questions
- Walk us through a neural rendering project you've worked on—what methods did you use and what were the key challenges?
- How would you approach evaluating whether improved visual fidelity in simulation actually translates to better autonomous system performance?