Principal Technical Product Manager — Quantum-HPC and AI Integration at QuEra Computing · LumiJobs

Responsibilities

QuEra's quantum computers (QPUs) will not be used in isolation. Our first gate-based machine Gemini is already deployed as part of the ABCI-Q supercomputer facility at AIST in Japan. Our first FTQC machine Libra, contracted to launch on Amazon Braket in 2028, will live within AWS’ vast managed compute platform and developer ecosystem. The subsequent machines of the Libra class and beyond are already being planned for on-prem and HPC-integrated deployments globally, to deliver value as compute resources inside classical environments: data centers, HPC facilities, AI infrastructures. Furthermore, the AI assistants and agents through which scientists and developers increasingly do their work, will need to harness our QPUs to improve them, and to discover and optimize hardware-efficient algorithms and applications for them.
We are hiring a Principal Technical Product Manager to own how QuEra’s quantum computers integrate with classical compute, in two directions. Outward: how a QuEra QPU becomes a first-class resource in HPC and AI infrastructure — scheduling, orchestration, hybrid CPU/GPU/QPU execution, data and results flow, and the partner stacks (AWS, NVIDIA, HPE, HPC centers) it has to fit. Inward: how AI systems reach the machine — the interfaces, tooling and workflows through which LLM-based assistants and agentic workflows discover, program and run jobs on QuEra hardware. You will work closely with the teams applying AI inside QuEra’s own machine operations; your focus is the user- and ecosystem-facing side. You own the strategy, requirements and roadmap for both directions, and this product area is expected to grow a team as partnerships convert into deployments.
What anchors both directions is the application space. You will collaborate with our applications and solutions scientists in the process of selecting two to three killer applications — hybrid quantum-classical computations that generate significant value to industry at the MegaQuOp-to-GigaQuOp scale of QPU — and let these applications drive the requirements for HPC-AI-quantum integration, rather than integrating for its own sake.
Year-one focus and deliverables
Killer applications and use-case mapping. Lead the selection, with the applications team, of two to three hybrid applications — in chemistry, materials, optimization, AI-for-science or beyond — that create significant industrial value at MegaQuOp-to-GigaQuOp scale, and map which applications and workflows need which level of quantum-classical co-processing on a three-to-five-year horizon, including the machine-to-machine interfaces and anticipated agentic workflows. Outcome: the applications selected and agreed, and driving the integration requirements.
Proof of concept. One of the killer applications, initially at small scale, running as a hybrid QPU/GPU/CPU workload in a partner HPC or AI environment, and an AI-driven workflow that programs and runs a job on a QuEra machine end to end. You lead this hands-on, with QuEra’s software engineering teams as the architecture partners, surfacing practical problems early.
Ecosystem and value-chain mapping. Who sits where in the HPC and AI stack — cloud providers, accelerator and system vendors, HPC centers, middleware and orchestration vendors, AI platform and agent-framework providers — and each one’s technical vision for quantum integration. Outcome: a complete ecosystem map and a QuEra position on which layers we own, partner on, or leave to others.
Strategic partnerships. Two to three partnerships established at the architecture level — joint architecture, integration model and value-chain positioning — secured through technical buy-in: their architects agreeing that our interfaces and reference architecture are the ones to build against.
Deployment and interface requirements. MVP requirements, derived from the killer applications, for a QuEra QPU as a node in an HPC/AI datacenter — networking, scheduling, identity and access, security and compliance, workload execution model, monitoring, facility parameters — and for the Quantum-Classical Integration Interfaces (APIs, SDKs, orchestration) that connect it. In parallel, a first specification of AI access to the machine: what an assistant or agent needs to discover a device, understand its capabilities and constraints, compose and validate a program, submit it and interpret the result — tool and API surfaces, machine-readable documentation and schemas, guardrails, cost and queue awareness. Outcome: both adopted into the platform and Libra roadmaps.
Own the integration strategy and roadmap for QuEra QPUs in HPC and AI environments and for AI interfaces to QuEra machines, with a phased path from simulators on HPC clusters, to joint classical-quantum scheduling, to native quantum accelerators on-site or over high-speed links.
Translate the killer applications and workflow needs into a practical quantum-classical execution model and into requirements that engineering can build against, working with engineering as the architecture partner.
Contribute the integration and AI-interface perspective to the products this work lands in: the QuEra platform, Gemini, Libra on Braket, and user tooling — so that cloud, on-prem, HPC-integrated and AI-mediated access share one service layer.

Principal Technical Product Manager — Quantum-HPC and AI Integration

Responsibilities

Qualification

Preferred

Benefits

Company

QuEra Computing

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