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AI for Quantum Operations Lead

QuEra Computing - Boston, MA, USA

Posted Jun 8, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
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Salary
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401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$61,842 U.S. median for this role
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.

Role

Role function
Operations From the posting source checked Jun 20, 2026
Seniority
Senior From the posting source checked Jun 20, 2026

Schedule

Shift type
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Weekend work
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Company

Company stage
Growth-stage From the posting source checked Jun 20, 2026

Application

Cover letter
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Assessment
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Deadline
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Where they hire

State eligibility is not yet verified.

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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