Research Engineer – Robotics, Embodied AI

Elorian AI

. Advance and evaluate visual reasoning, 3D spatial understanding, and physical affordance capabilities within multimodal foundation models .

Posted 10/30/2026full-timePalo Alto • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in robotics, particularly in robotic perception, planning, and control, while effectively applying multimodal foundation models and machine learning techniques to solve complex technical challenges in real-world applications.

Highest-signal resume keywords

Robotic PerceptionMultimodal Foundation ModelsRobotics Simulation EnvironmentsMachine LearningRobotic Planning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Robotic PerceptionRobotic PlanningControl SystemsRobot LearningMachine LearningVision-Language-Action ModelsTrajectory Annotation3D Spatial ReasoningAffordance ModelingHierarchical Reasoning

Soft Skills

Problem-SolvingCollaborationResearch Skills

Tools & Technologies

Isaac SimMuJoCoDeep Learning FrameworksTransformer Architectures

Industry Keywords

RoboticsMultimodal ArchitecturesFoundation Model ResearchAutonomous AgentsPhysical Systems

About the role

Key responsibilities & impact

Advance and evaluate visual reasoning, 3D spatial understanding, and physical affordance capabilities within multimodal foundation models
Design scalable robotics data pipelines for data collection, teleoperation, trajectory annotation, automated quality assessment, and evaluation frameworks
Investigate and implement robotic task planning, hierarchical reasoning, and decision-making using multimodal foundation models and Vision-Language-Action architectures
Collaborate with foundation model researchers and robotics partners to identify real-world bottlenecks, evaluate model capabilities, and build foundational AI solutions for robotics

Requirements

What you’ll need

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
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