You will serve as a technical anchor, designing and evolving enterprise-grade architecture for complex SaaS systems. You will lead efforts to improve system reliability, mentor senior engineering talent, and set technical standards across the organization.
Own and lead the design of the autonomy software stack, including perception, localization, and scanning pipelines for MHE Vision. Collaborate with hardware and ML teams to ensure reliable inventory data capture in dynamic warehouse environments.
Own and lead the design of the core autonomy software stack, including perception, localization, and scanning pipelines for drone platforms. Collaborate with hardware and ML teams to ensure reliable performance in dynamic warehouse environments.
Own the ROS2 and Zenoh communications layer and Docker infrastructure across drone and MHEV platforms. Develop diagnostics tooling and a roadmap to converge separate product architectures into a unified platform.
Architect and build a greenfield, multi-layer data warehouse and semantic layer to separate analytical workloads from production traffic. Establish data governance, tenant isolation, and traceability between structured records and unstructured drone imagery.
Own the end-to-end UX design for operator apps and AI-driven dashboards used in warehouse environments. Conduct on-site user research at customer facilities to translate real-world operational needs into intuitive software interfaces.
The Director will define and own the Machine Learning strategy and technical roadmap, leading and growing the Machine Learning and FPT teams while establishing a culture of rigor and production-quality delivery. Key tasks include driving improvements to core computer vision models and building out MLOps infrastructure for model training, deployment, and monitoring.
The Principal ML Scientist will advance core computer vision model performance for warehouse inventory scanning across drone and MHE Vision platforms, owning the full ML lifecycle from research through production deployment and monitoring. This includes collaborating on model optimization for various inference targets and providing technical leadership and mentorship to the ML team.
