Robotics Research Engineer (f/m/d)

About the role

At NXAI, we are building the future of AI from our hub in Linz, Austria. Our mission is to turn advanced research into real-world impact, with a focus on next-generation xLSTM models and their industrial applications. Working at the intersection of research and industry, we are creating a leading center for AI innovation in the region. As a Robotics Research Engineer at NXAI, you will take our next-generation sequence models out of the lab and into the physical world. You will work at the intersection of foundation model research and embodied AI, building the perception, reasoning and action pipelines that let robots understand instructions, interpret their environment and act reliably in real world settings. This is a hands-on research role: you will be as comfortable fine-tuning a Vision-Language-Action (VLA) model as you are debugging a policy on real hardware. You will work closely with our core research team to explore how xLSTM can push robotic foundation models beyond today's state of the art. Key Responsibilities - Own physical robot and workstation uptime, including the integration of new sensors, compute units, and end-effectors, while iteratively modifying setups to improve experimental throughput. - Architect and maintain fault-tolerant, high-throughput pipelines for multi-modal data ingestion, incorporating automated data validation, quality scoring, and versioning. - Establish standardized, reproducible real-world evaluation protocols and metrics (success rate, task completion time, out-of-distribution robustness) to systematically benchmark policy progression. - Conduct end-to-end root-cause analysis across the entire robotics stack, diagnosing bottlenecks spanning physical sensors, actuators, network/communication overhead, inference runtimes, and training distribution shift. - Serve as the critical feedback loop between physical deployment and algorithm design, turning empirical hardware failures and edge cases into actionable research directions and data strategy updates. - Fine-tune, optimize, and deploy modern learning-based policies (e.g., imitation learning, diffusion policies, vision-language-action models) into real-time, low-latency control loops on physical hardware.