AI for Quantum Operations Lead

The AI for Quantum Operations Lead owns the roadmap and execution strategy for AI-assisted calibration, diagnostics, prediction, and recovery across quantum systems, ensuring that AI improves machine uptime, calibration speed, and operator decision‑making while deterministic control and safety software remain authoritative. Key Responsibilities Define and drive the AI operations roadmap across calibration optimization, atom image/readout analysis, drift prediction, root‑cause diagnosis, and recovery recommendation. Partner with quantum systems, controls, software, hardware, and ML teams to identify high‑value workflows where AI can safely propose, rank, predict, or optimize. Establish the bounded‑AI operating model: AI provides recommendations or constrained optimizations, while deterministic control software enforces timing, hardware limits, validation, rollback, and safety logic. Prioritize AI pilots for Quokka, Calibration Manager, telemetry systems, readout pipelines, and QPU operations workflows. Own requirements for dataset traceability, model validation, observability, offline replay, deployment gates, and operator‑facing explainability. Translate machine‑performance pain points into measurable AI/ML objectives such as reduced calibration time, fewer failed jobs, faster recovery, improved readout quality, and better drift detection. Coordinate cross‑functional execution, staffing needs, milestones, risk reviews, and stakeholder communication. Required Background Strong technical leadership experience in AI/ML, controls, robotics, scientific instrumentation, or complex hardware operations. Experience bringing ML models into production environments where reliability, safety, traceability, and human/operator trust matter. Ability to work across software, hardware, physics, and operations teams. Strong systems thinking; understands where AI should help, where deterministic software must remain in charge, and how to design the boundary between them. Preferred Background Experience with Bayesian optimization, active learning, time‑series forecasting, computer vision, anomaly detection, or root‑cause analysis. Familiarity with calibration workflows, lab automation, telemetry systems, or hardware‑in‑the‑loop validation. Exposure to quantum computing, neutral atoms, optical systems, embedded control, or real‑time systems. Success Measures Clear AI operations roadmap with owners, milestones, and safety gates. First bounded AI pilots deployed into calibration or readout workflows. Measurable reduction in calibration effort, diagnosis time, drift‑related failures, or recovery time. Strong governance around model validation, data provenance, deployment approval, and operator trust. QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. #J-18808-Ljbffr QuEra Computing