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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 trust 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 is a backend-heavy role focused on large-scale distributed systems and ML infrastructure.
Key responsibilities:
- Design and build scalable closed-loop simulation systems using neural reconstruction
- Architect systems to process and manage large-scale data pipelines
- Develop infrastructure for ML workflows: training pipelines, evaluation/validation systems, and model inference
- Design 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 product teams on end-to-end solutions
- Own critical systems and influence architecture and product decisions
The role is ideal for engineers who thrive at the intersection of distributed systems and machine learning, and who want to scale a state-of-the-art product while solving hard data and platform challenges in physical AI.
Applied Intuition operates as an in-office company with an expectation that full-time employees work from their office 5 days per week, though occasional flexibility is available for morning remote work or schedule adjustments.
REQUIREMENTS:
- 5+ years of experience shipping complex, large-scale software
- Bachelor's degree in computer science, computer engineering, or equivalent practical experience
- Proven track record designing, building, and operating scalable distributed systems, data pipelines, and data storage solutions (SQL, NoSQL, vector databases, data lakes)
- Experience with cloud platforms (AWS, GCP, or Azure) and containerized systems (Docker, Kubernetes)
- Backend development experience in Python and/or 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 (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
