Dyna Robotics
Research Engineer/ Scientist
About this role
Dyna Robotics is seeking a Research Engineer to develop embodied AI and robot learning algorithms that enable general-purpose robots to operate effectively in unstructured environments. You'll own the full pipeline from model design through real-world deployment, working with multimodal datasets and cutting-edge architectures like Vision-Language-Action models and diffusion-based learning.
What you'll do
- Design and implement robot learning algorithms (reinforcement learning, imitation learning, diffusion models) for complex manipulation tasks
- Architect and scale multi-modal foundation models and video generation systems for improved world modeling
- Manage end-to-end research-to-production pipeline including data curation, training, and hardware deployment
- Evaluate and iterate on models using physical robot fleet in real-world customer environments
- Collaborate with AI, hardware, and teleoperation teams to optimize system performance
- Write production-grade research code that scales across GPU clusters
What they're looking for
- Robot learning (imitation learning, reinforcement learning, diffusion models)
- Vision-Language-Action (VLA) models and video generation architectures
- Python and PyTorch (or JAX)
- Deep learning deployment on physical robots
- Robotics simulation tools (MuJoCo, Isaac, Drake)
- Large-scale transformer architectures
- Classical robotics and control systems
- Model optimization for edge device inference
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Dyna Robotics
Dyna Robotics builds AI-driven robotics platforms that combine embodied AI foundation models with spatial intelligence to enable robots to navigate and operate autonomously across customer sites. The company is hiring software engineers, infrastructure specialists, QA testers, and field deployment engineers to scale its robot deployment systems, training infrastructure, state estimation capabilities, and customer operations.
View all jobs at Dyna RoboticsLikely interview questions
- Walk us through a robotics project where you deployed a deep learning model on physical hardware. What were the biggest sim-to-real gaps you encountered, and how did you solve them?
- Describe your experience building or fine-tuning Vision-Language-Action (VLA) models. How would you approach scaling one for high-DOF manipulation tasks?