Software Engineer, Applied Intuition

• 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
• 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
• 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. You may be just the right candidate for this or other roles

Desirable

• 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
• 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