Principal Architect - Reinforcement Learning (Physical AI) at Caterpillar in Irving, TX

Job description

Career Area: Technology, Digital and Data Job Description: Your Work Shapes the World at Caterpillar Inc. When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it. Help Build the Future of Caterpillar – At Caterpillar, technology always has a purpose, which is to solve our customers’ toughest challenges. Through Cat Technology, we are solving problems by building the intelligence layer that connects machines, data, and people to make jobsites safer, more productive, and more sustainable. By combining deep domain expertise in physical systems with software, connectivity, autonomy, and AI, we deliver solutions that work in the real world—on real jobsites, at global scale. You’ll build and deploy against one of the most unique data foundations—over 1.6 million connected assets generating real-world data daily. These data and platform capabilities are enabling the development of AI models, edge computing architectures, and software systems that scale across fleets, products, and industries. The result will be a new generation of machines that continuously learn, improve, and deliver performance at scale. Be Part of What’s Next in Autonomous Construction Sites Construction autonomy is one of the most complex challenges in applied AI, and at Caterpillar, advancements in physical AI, simulation, sensing, and edge computing are turning things that once felt impossible—intelligent machines operating in dynamic jobsites—into reality. Our connected ecosystem brings together massive volumes of high-quality data to create a foundation where engineers like you can build and deploy against. If this work motivates you, we invite you to join our team. In these roles, you’ll work at the intersection of the physical and digital worlds. You’ll help design and deliver intelligent systems that enable machines to perceive their environment, make informed decisions, and support safer, more productive operations. Apply today to build the new era of construction autonomy at Caterpillar. Job Summary: The Learning Systems Architect owns how the machine learns — the end-to-end system that turns simulation, digital twins, and fleet data into high-performance, field-proven policies and models. The Principal Architect – Reinforcement Learning (RL) Systems is responsible for the architecture of the learning system that produces Caterpillar’s Physical AI models: the reinforcement learning, imitation learning, and foundation-model post-training pipelines that train perception, prediction, planning, and control policies for excavators, dozers, loaders, and haul trucks. Where the Simulation Engine architect owns the runtime the policy trains in, the Digital Twin architect owns the fidelity of what it trains against, and the AI Factory architect owns the compute and data platform it trains on, this role owns the learning problem itself: how tasks are formulated as observations, actions, rewards, and curricula; which policy and model architectures are used; how large-scale closed-loop training in simulation is structured; how sim-to-real transfer is achieved and measured; and how learned policies are evaluated, made safe, and deployed onto machines. The architect defines the reference architecture for learning-based autonomy at Caterpillar: Gym-style task and environment specifications on top of Isaac Lab / Isaac Sim, Omniverse, and hybrid neural simulation (NVIDIA Cosmos-class world models); massively parallel GPU-based RL (PPO, SAC, GRPO and successors) and imitation learning (behavior cloning, distillation, offline RL) at fleet-data scale; vision-language-action and foundation-model post-