Software Engineer - Neural Simulation
About The Position
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co. We are looking for software engineers to help 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 distributed systems that transform real-world data into simulation environments and generate high-quality labeled data at scale, working alongside senior engineers on system design across compute, storage, and data pipelines. This role is ideal for engineers who want to grow at the intersection of large-scale distributed systems and machine learning, and who are excited to help scale a state-of-the-art product while building new capabilities that solve the hardest data and platform gaps in Physical AI.
Requirements
- 2+ years of experience shipping production software
- A minimum of a 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 (such as SQL, NoSQL, or data lakes)
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerized systems (Docker, Kubernetes)
- Experience with backend development in languages such as Python and Go
- Solid software engineering fundamentals and strong problem-solving skills
Nice To Haves
- Experience with distributed systems at scale
- Experience building or supporting ML training and serving infrastructure
- Experience with GPU workloads and 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
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, including: Training pipelines, Evaluation and validation systems, Model inference pipelines
- Implement 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
- Own features and components end to end, from design through deployment, and contribute to architecture discussions
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
