AI for Quantum Operations Lead

QuEra Computing, Inc. • Boston, MA

12d

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed