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Apptronik is a human-centered robotics company developing AI-powered humanoid robots (Apollo) to support humanity across manufacturing, logistics, healthcare, and beyond. As Staff Simulation Architect for the Core Simulation Platform, you will own the architecture of a mission-critical distributed system that powers policy training, controls validation, and CI/CD integration testing for the entire robotics stack.
You'll architect and scale cloud-native pipelines capable of generating millions of experience hours per day, designing high-performance distributed systems that enable rapid iteration for reinforcement learning, controls, and perception teams. Your responsibilities include defining core platform abstractions and APIs to keep the system extensible as new physics, sensor, and rendering capabilities are added; setting technical direction on performance, reliability, and scalability; identifying and resolving systemic bottlenecks in a large, high-throughput system; and defining the platform roadmap while reviewing major design proposals.
You'll partner closely with Controls, AI Research, and Perception leadership to translate their evolving needs into coherent, extensible architecture. You'll establish architectural standards and mentor senior engineers on system design, bringing deep distributed systems expertise into a robotics context.
Required: 7+ years of software engineering experience with significant time in software architecture or technical leadership on complex, large-scale systems. Excellent C++ and/or Python skills applied to large-scale system architecture. Deep experience architecting distributed, high-throughput, or real-time systems (from robotics, gaming, simulation, high-frequency trading, or similar domains). Proven experience architecting workloads on AWS, GCP, or Azure with distributed computing frameworks like Ray or Kubernetes. Strong software development practices: CI/CD, automated testing, code quality. Track record of making and defending architectural trade-offs and communicating them clearly.
Bonus: Understanding of robotics concepts (kinematics, dynamics, controls, system identification); experience with modern robotic simulators (Isaac Lab, MuJoCo); or understanding of reinforcement learning with research or production experience. Bachelor's, Master's, or PhD in Computer Science, Engineering, or related field (or equivalent).
