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Applied Intuition is a $15B-valued Silicon Valley company (founded 2017) building digital infrastructure for physical AI across automotive, defense, trucking, construction, mining, and agriculture. Eighteen of the top 20 global automakers and the US military rely on their solutions.
You will join the Neural Simulation team to design and develop systems that transform real-world driving data into high-fidelity simulation environments. This backbone product powers large-scale reconstruction, synthetic data generation, and log augmentation, along with ML pipelines for training and validating autonomy systems.
Key responsibilities:
- Build and maintain scalable systems for Neural Simulation, including closed-loop simulation and log augmentation workflows
- Develop services and data pipelines that process and manage large-scale data
- Contribute to infrastructure for ML workflows: training pipelines, evaluation/validation systems, and model inference pipelines
- Implement and optimize storage solutions for structured, unstructured, and multimodal data (sensor, 3D, logs)
- Improve system reliability, observability, and performance across distributed services
- Collaborate with Infra, Autonomy, Research, and other product teams on end-to-end solutions
- Own features and components from design through deployment; contribute to architecture discussions
This role is ideal for engineers excited to grow at the intersection of large-scale distributed systems and machine learning, helping scale a state-of-the-art product while solving critical data and platform challenges in Physical AI.
Applied Intuition is an in-office company with the expectation that full-time employees work from their office 5 days per week, with flexibility for occasional remote work and schedule adjustments as needed.
REQUIREMENTS:
- 2+ years of experience shipping production software
- Bachelor's degree in computer science, computer engineering, or equivalent practical experience
- Experience building backend services or data pipelines and working with data storage systems (SQL, NoSQL, data lakes)
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerized systems (Docker, Kubernetes)
- Backend development experience in Python and/or Go
- Solid software engineering fundamentals and strong problem-solving skills
NICE TO HAVE:
- Experience with distributed systems at scale
- Experience building or supporting ML training and serving infrastructure
- Experience with GPU workloads and batch orchestration (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
