Member of Technical Staff – Agents

About Dimensional

Dimensional is building the foundation for physical intelligence in general-purpose robotics. We develop a unified framework that enables robots to perceive, reason, learn, and act across diverse real-world environments and embodiments. Dimensional’s mission is to make physical intelligence programmable, scalable, and accessible -- accelerating the development of generalist robots that can adapt, learn, and perform reliably in the real world.

Our platform sits between robot hardware and AI models, providing a modular, composable architecture for training and deploying robotic intelligence at scale. By abstracting hardware differences and standardizing core capabilities—such as perception, spatial understanding, decision-making, and control—Dimensional allows developers and partners to build, transfer, and operate robotic skills efficiently across platforms.

About The Role

We’re hiring an LLM Engineer to build intelligent agent systems that interact with real-world robotic platforms. This role sits at the intersection of cutting-edge agent development, multimodal reasoning, and production-grade software engineering.

You’ll help develop an advanced agent harness designed to rival leading coding agents like Claude Code and Codex, while leveraging the unique capabilities of Dimensional OS. Unlike traditional coding agents, our systems must reason over massive volumes of real-world sensor data, extract meaningful insights, and answer complex, context-dependent questions, all while efficiently managing context windows and maintaining performance across long-horizon tasks.

We're looking for engineers who thrive at the intersection of research and production, with the ability to experiment rapidly, solve complex technical challenges, and ship reliable systems, not just prototypes. You might be a former founder, an early engineer at an agent-focused startup, or someone deeply immersed in the modern agent ecosystem with exceptional software engineering fundamentals.

A few examples of what you might work on

• Evaluate agent performance by modeling real-world robotics scenarios in simulation environments such as MuJoCo and Habitat.

• Develop advanced agent harnesses capable of processing massive volumes of sensor data while maintaining high performance across long trajectories.

• Build multimodal reasoning and fact-finding systems that integrate point clouds, images, audio, and text to extract meaningful insights.

• Design agent memory systems that preserve relevant information across runs, including memory migrations, context organization, and orchestration state management.

• Develop holistic representations of 3D environments using scene reconstruction and spatial reasoning techniques, enabling agents to efficiently query, interpret, and reason about their surroundings.