Assess which robotics and edge AI workloads are a strong fit for GPU acceleration by profiling bottlenecks and distinguishing compute-bound problems from sensor IO, memory bandwidth, network, latency, power, or orchestration constraints.
Build and adapt robotics demos, sample code, reference architectures, lab material, and technical guides that help developers evaluate and adopt NVIDIA technologies.
Guide developers on NVIDIA cloud-to-edge platforms, accelerated AI workflows, simulation, model optimization, edge inference, and deployment patterns.
Run robotics-focused code labs, workshops, architecture reviews, office hours, developer sessions, technical webinars, and community programs.
Partner with internal NVIDIA teams to support priority robotics accounts and unblock technical adoption.
Build ecosystem engagement across robotics startups, industrial automation partners, universities, and research communities.
Capture field insights on hardware requirements, software tooling gaps, deployment blockers, and competitive dynamics.
What We Need To See
Bachelor's degree in robotics, computer science, electrical engineering, mechanical engineering, AI, or a related technical discipline, or equivalent experience.
8+ years of experience in technical Developer Relations, robotics, autonomous systems, edge AI, embedded systems, computer vision, simulation, solution architecture, or technical ecosystem development.
Understanding of robotics software stacks, AI perception, sensor fusion, edge deployment, model optimization, and production constraints for physical systems.
Hands-on programming experience in Python and C++ with familiarity in Linux-based development, robotics middleware, sensor data pipelines, and edge deployment workflows.
Familiarity with cloud-to-edge workflows, containers, Linux, embedded platforms, APIs, and modern AI development practices.
Ability to profile and reason about robotics workloads, including GPU suitability, compute intensity, memory bandwidth, sensor IO, latency, throughput, batching, power, thermal limits, and cost/performance tradeoffs.
Ability to engage deeply technical engineering teams and senior business stakeholders, with credibility in live demos, technical troubleshooting, architecture reviews, and developer education.
Hands-on exposure to NVIDIA robotics and edge AI platforms such as Isaac Sim, Isaac ROS, Isaac Lab, Omniverse Replicator or synthetic data workflows, Jetson, TensorRT, Triton Inference Server, CUDA, Nsight Systems, Nsight Compute, and NGC containers.
Ability to demonstrate simulation-to-real workflows, robot perception pipelines, synthetic data generation, edge inference optimization, and deployment patterns across cloud, workstation, and embedded environments.
Familiarity with NVIDIA software used for physical AI workloads, including model optimization, sensor data processing, computer vision acceleration, ROS 2 integration, and deployment on edge systems as well as ability to build robotics-focused demos, lab material, and reference architectures that show how NVIDIA platforms accelerate perception, autonomy, simulation, and physical AI development.
Ways To Stand Out from the Crowd
Hands-on experience with robotics frameworks, simulation environments, computer vision, synthetic data, digital twins, or autonomy stacks.
Experience creating workload qualification frameworks, benchmark plans, or technical decision guides that help robotics developers decide when GPU acceleration is the right fit.
Track record as a technical DevRel practitioner, including public talks, workshops, GitHub samples, blogs, tutorials, reference architectures, or developer community programs.
Experience with warehouse automation, industrial robotics, AMRs, autonomous machines, smart manufacturing, or logistics automation.
Ability to translate robotics developer needs into scalable ecosystem programs and product feedback.
