AI for Quantum Operations Lead

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Market context

Median wage (BLS OEWS)

$61,842 national median

Projected growth (BLS Employment Projections)

+1.9% - Slower

Matched to SOC 11-1021 - Operations aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

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About this role

AI for Quantum Operations Lead Boston, MA, USA Role Summary 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

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