Embodied AI VLA Research Engineer

In this role you will work on the research training optimization and real-world deployment of embodied AI models including Vision-Language-Action (VLA) models World Models and related robotics foundation models.

You will work across multimodal perception robot control long-horizon task planning large-scale robot data and model deployment. This is a highly hands-on role that involves both model development and real-world robotic system integration.

• Research and develop Vision-Language-Action (VLA) World Model and other embodied AI foundation models.

• Work on areas including:

  • Robotic manipulation

  • Multimodal perception and control

  • Long-horizon task planning

  • Vision-language-action reasoning

  • Robot-environment interaction

• Develop and optimize models for real-world robotic applications.

• Translate research ideas into practical models and systems that can operate reliably on physical robots.

• Train and optimize embodied AI foundation models using large-scale real-robot datasets and egocentric human demonstration data.

• Design approaches for incorporating multimodal inputs such as:

  • Vision

  • Force

  • Tactile sensing

  • Proprioception

  • Other robot and environmental signals

• Develop and evaluate multimodal fusion architectures.

• Conduct model training evaluation benchmarking and performance optimization.

• Analyze model performance and identify opportunities to improve training efficiency generalization and real-world performance.

• Build and improve large-scale robotics data pipelines for model training.

• Develop processes for:

  • Data cleaning

  • Data filtering

  • Resampling

  • Data augmentation

  • Data quality evaluation

• Design scalable data pipelines capable of supporting large volumes of robot and human demonstration data.

• Work closely with data and robotics teams to improve dataset quality and training efficiency.

• Deploy trained models to physical robot systems.

• Perform real-world robot debugging testing and performance optimization.

• Diagnose issues across models sensors software and robotic hardware.

• Iterate between model training and real-world testing to improve system performance.

• Help ensure models operate reliably and consistently in real-world environments.

• 1 years of relevant industry or research experience in machine learning robotics computer vision embodied AI or a related field.

• Strong understanding of deep learning and modern machine learning methods.

• Experience with PyTorch or similar deep learning frameworks.

• Experience training and evaluating machine learning models.

• Strong programming skills in Python and familiarity with relevant ML/robotics tooling.

• Understanding of multimodal learning computer vision robotics or related areas.

• Ability to work in a fast-paced startup environment and take ownership of technical problems from research through implementation.

• Strong problem-solving and debugging skills.

Real-World Robotics Deployment

• Experience deploying and debugging machine learning models on physical robot systems.

• Ability to bring models from development into real-world robotic environments.

• Experience troubleshooting and stabilizing robotic systems in production or experimental environments.

Multimodal / VLA Models

• Experience working with force tactile or other multimodal sensing.

• Experience designing or training multimodal fusion models.

• Hands-on experience with VLA models including model design training evaluation or real-world applications.

• Experience with robotic manipulation or embodied AI systems.

Large-Scale Distributed Training

• Experience with large-scale distributed model training.

• Familiarity with DDP DeepSpeed FSDP or similar distributed training frameworks.

• Experience optimizing training performance GPU utilization memory usage or training throughput.

• Experience working with large-scale datasets and distributed data pipelines.

We are looking for an engineer who is excited about the intersection of foundation models and physical robotics.

The ideal candidate is:

• Hands-on and comfortable moving between research coding experimentation and real-world robot testing.

• Interested in solving problems that cannot be addressed through simulation or software alone.

• Comfortable working with large-scale datasets and modern foundation-model architectures.

• Able to take ownership of a problem from data training evaluation deployment real-world iteration.

• Comfortable working in an early-stage environment where priorities can move quickly.

• Curious about emerging VLA World Model and embodied AI research and able to translate new ideas into working systems.