Senior Software Engineer - Neural Simulation

We are looking for senior software engineers to build the backbone of Neural Simulation, our state-of-the-art product for turning real-world driving data into high-fidelity simulation environments. As part of this team, you will design and develop the systems that power large-scale reconstruction, synthetic data generation, and log augmentation, along with the ML pipelines used to train and validate autonomy systems. You will work on highly distributed systems that transform real-world data into simulation environments and generate high-quality labeled data at scale, where careful system design across compute, storage, and data pipelines is essential. 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.

Responsibilities

Design and build scalable closed loop simulation systems using Neural Reconstruction; Architect systems that process and manage large-scale data; Develop infrastructure for ML workflows, including training pipelines, evaluation and validation systems, and model inference pipelines; Design and optimize storage solutions for structured, unstructured, and multimodal data (e.g., sensor, 3D, logs); Improve system reliability, observability, and performance across distributed services; Collaborate closely with Infra, Autonomy, Research and other product teams to deliver end-to-end solutions; Take ownership of critical systems and influence architecture and product decisions

Requirements

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; 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); Experience with cloud platforms (AWS, GCP, or Azure) and containerized systems (Docker, Kubernetes); Experience with backend development in languages such as Python and Go; Strong system design and problem-solving skills

Nice to have

Experience building or supporting ML training and serving infrastructure; Experience with GPU workloads and large-scale batch orchestration (e.g., Kubernetes jobs, Ray, Airflow); 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