Research Engineer - Robotics & Embodied AI

About the Role

As a foundation model company, Elorian aims to empower robotics developers through advanced visual reasoning, spatial understanding, and high-quality robotics data solutions. We are looking for researchers and engineers with a background in robotics who want to apply their domain expertise to foundation-scale AI.

Instead of focusing solely on isolated robot hardware or narrow policies, you will collaborate closely with our core foundation model research team to infuse physical-world priors into our models, address real-world robotics challenges, and develop foundational capabilities that generalize across a broad spectrum of robotics applications.

What You Will Do

• Develop Visual & Spatial Intelligence for Robotics: Advance and evaluate visual reasoning, 3D spatial understanding, and physical affordance capabilities directly within our multimodal foundation models to support downstream robotic perception and control.
• Architect Robotics Data Pipelines: Design scalable data pipelines-including data collection, teleoperation and trajectory annotation, automated quality assessment, and evaluation frameworks for multi-modal robotic data.
• Explore Foundation-Driven Embodied Reasoning: Investigate and implement novel approaches to robotic task planning, hierarchical reasoning, and decision-making powered by multimodal foundation models and Vision-Language-Action (VLA) architectures.
• Bridge Foundation Models & Robotics Applications: Collaborate closely with core foundation model researchers and robotics partners to identify real-world bottlenecks, evaluate model capabilities, and build impactful foundational AI solutions for the robotics ecosystem.

What We Are Looking for

• Strong background in robotics (research or engineering), with first-hand experience in robotic perception, planning, control, or robot learning.
• Genuine excitement to transition robotics domain knowledge into large-scale deep learning, multimodal architectures, and foundation model research.
• Solid experience with machine learning and hands-on familiarity with modern LLMs, multimodal foundation models (VLMs), or transformer architectures.
• Strong research and problem-solving skills, with the ability to independently investigate open-ended, complex technical challenges.

Nice to Have

• Experience with robotics simulation environments (e.g., Isaac Sim, MuJoCo) for large-scale data generation, policy evaluation, or model pre-training.
• Experience with robotics datasets, trajectory annotation, task decomposition, and benchmark evaluation for physical systems.
• Experience applying, fine-tuning, or developing LLMs, VLMs, or Vision-Language-Action (VLA) models for robotics problems.
• Background working with real-world robotic systems (manipulation, navigation, or autonomous agents) and understanding the practical limits of deploying policies to physical hardware.
• Deep expertise in robotic planning, 3D spatial reasoning, affordance modeling, or embodied intelligence.