LG Electronics
Embodied AI Engineer
About this role
LG's Emerging Technology Lab seeks an Embodied AI Engineer to design and deploy robot intelligence systems for the Zero-labor Home initiative. You'll bridge frontier AI research and consumer robotics by building infrastructure, training pipelines, and deployment stacks that turn foundation models into real-world robot capabilities across LG's next-generation platforms.
What you'll do
- Implement, optimize, and debug model architectures (VLAs, diffusion policies, world models) from prototype to production-ready systems
- Build and maintain scalable training and evaluation infrastructure for Robot Foundation Models and on-robot deployment
- Design large-scale data pipelines integrating simulation, generative AI, teleoperation, and real-world robot data collection
- Debug real-world robot failures and close sim-to-real gaps through hands-on hardware platform bring-up
- Collaborate with global research teams and hardware partners to integrate embodied AI into LG's consumer ecosystem
- Contribute engineering input to technical roadmaps and translate research concepts into shipped systems
What they're looking for
- Python and/or C++ software engineering across full ML stack
- Robot Foundation Models and diffusion/flow-matching policy training
- Data pipeline design for robot learning systems
- Robot system infrastructure (manipulators, mobile robots, sensors)
- Simulation and real-world robotics integration
- Technical communication and cross-functional collaboration
- Robot learning research literature familiarity
- World models and spatial intelligence concepts
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LG Electronics
LG Electronics develops HVAC control systems and infotainment/telematics products for the automotive and appliance markets. The company is hiring Software Engineers and Validation Engineers to lead system integration testing, controls validation, and product specification verification across its North American and global operations.
- Website
- lg.com
Likely interview questions
- Walk us through a time you deployed a robot learning model from simulation to real hardware—what were the key sim-to-real gaps and how did you close them?
- Describe your experience building or maintaining data pipelines for robot training. How did you handle data quality and diversity at scale?