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Lead and manage a hands-on team of data operators and interns in a physical robot training facility (NEURAGym). Optimize shop-floor processes, track KPIs, coordinate staffing for data collection campaigns, support hiring, and liaise with stakeholders and robotics/AI teams to resolve technical challenges and shape hiring and skills development.

Intern will support the ERP team by researching and piloting AI models/services, integrating AI APIs into Business Central, building reports and dashboards from ERP data, preparing KPIs and data models, developing AI-powered reporting features, testing Business Central configurations, and documenting use cases and integrations.

Lead a sub-team of AI engineers to set technical direction, transition research to production on robot hardware, define evaluation and deployment standards, collaborate cross-functionally, unblock engineering work, and grow team capability.

Lead commercialization and go-to-market strategy for NEURA Robotics solutions. Develop scalable business models, translate technical capabilities into market-ready products, analyze win/loss data, identify growth opportunities, align Product, Sales, and Partner teams, support Account Executives, and drive successful launches and scaling of partner-driven solutions.

Own the top-level hardware architecture for a humanoid robot platform: define compute, power, communication, and sensing architectures; translate requirements into hardware constraints; govern standards; select technologies; ensure DFM/DFT and manufacturability; integrate with software/AI teams; mentor engineers.

Lead technical architecture for the full robot communication stack from real-time drive buses through middleware to cloud connectivity. Own API governance, fieldbus protocol integration, DDS/ROS2 standards, gRPC/protobuf contracts, and hands-on implementation. Write RFCs, lead cross-team reviews, mentor engineers, and reduce knowledge concentration with design artifacts.

Design and maintain scalable automated and human-in-the-loop annotation pipelines for multimodal robot data. Ingest and structure video, depth, proprioception, gripper states, and language; develop VQA and language-grounded annotation workflows; apply pre-labeling with foundation models; enforce data quality, curation, and bias mitigation to produce training-ready datasets.