Senior Software Engineer (Neural Simulation)

Join our team as a Senior Software Engineer specializing in Neural Simulation. In this role, you will design and develop systems that power large-scale reconstruction, synthetic data generation, and log augmentation. You will work on highly distributed systems that transform real-world data into simulation environments and generate high-quality labeled data at scale. This position is ideal for engineers who thrive at the intersection of large-scale distributed systems and machine learning.

• Design and develop systems for large-scale reconstruction, synthetic data generation, and log augmentation.
• Architect and build scalable closed-loop simulation systems using Neural Reconstruction.
• Collaborate with cross-functional teams to deliver end-to-end solutions and take ownership of critical systems.

• This role is ideal for engineers who thrive at the intersection of large-scale distributed systems and machine learning, and who are excited to scale a state-of-the-art product while building new capabilities that solve the hardest data and platform gaps in Physical AI
• Experience with backend development in languages such as Python and Go
• A proven track record of designing, building, and operating scalable distributed systems, data pipelines, and data storage solutions (such as SQL, NoSQL, vector databases, or data lakes)
• Strong system design and problem-solving skills
• Experience with cloud platforms (AWS, GCP, or Azure) and containerized systems (Docker, Kubernetes)
• 5+ years of experience shipping complex, large-scale software
• A minimum of a Bachelor's degree in computer science, computer engineering, or equivalent practical experience
• Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway
• Experience with GPU workloads and large-scale batch orchestration (e.g., Kubernetes jobs, Ray, Airflow)
• Experience building or supporting ML training and serving infrastructure
• Experience with synthetic data generation or data augmentation for ML
• Familiarity with computer vision, 3D reconstruction (e.g., Gaussian Splatting), or sensor simulation (camera, LiDAR, radar)
• Experience with autonomous driving or robotics systems