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Hpc On Azure Jobs (NOW HIRING)

University of New Mexico

HPC Cloud Engineer

Albuquerque, NM · On-site

$8.5K - $12K/mo

Hands-on experience with Linux system administration and management in research or high-performance ... Experience with research cloud governance and environments , including AWS Control Tower and Azure ...

Institute of Foundation Models

HPC Engineer

Sunnyvale, CA · On-site

$150K - $300K/yr

Preferred Qualifications • Slurm. • GPU infrastructure. • AWS, Azure, or GCP. • Grafana ... The actual compensation offered may vary within this range depending on individual qualifications ...

Deloitte

HPC AI Solution Architect (S2S)

Atlanta, GA · On-site

Recruiting for this role ends on 10/3/2026. Work you'll do As a Lead Cloud Integrated Infra ... Experience executing NVIDIA co-sell motions with OEMS (Dell, HPC, Lenovo), CSPs ( AWS, Azure ...

Deloitte

HPC AI Solution Architect (S2S)

Houston, TX · On-site

Recruiting for this role ends on 10/3/2026. Work you'll do As a Lead Cloud Integrated Infra ... Experience executing NVIDIA co-sell motions with OEMS (Dell, HPC, Lenovo), CSPs ( AWS, Azure ...

Deloitte

HPC AI Solution Architect (S2S)

Atlanta, GA · On-site

Recruiting for this role ends on 10/3/2026. Work you'll do As a Lead Cloud Integrated Infra ... Experience executing NVIDIA co-sell motions with OEMS (Dell, HPC, Lenovo), CSPs ( AWS, Azure ...

Deloitte

HPC AI Solution Architect (S2S)

Tampa, FL · On-site

Recruiting for this role ends on 10/3/2026. Work you'll do As a Lead Cloud Integrated Infra ... Experience executing NVIDIA co-sell motions with OEMS (Dell, HPC, Lenovo), CSPs ( AWS, Azure ...

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How much do hpc on azure jobs pay per hour?

As of Sep 13, 2026, the average hourly pay for hpc on azure in the United States is $58.40, according to ZipRecruiter salary data. Most workers in this role earn between $52.88 and $65.62 per hour, depending on experience, location, and employer.

What is HPC on Azure?

HPC on Azure refers to High Performance Computing solutions that leverage Microsoft Azure's cloud infrastructure to run complex and large-scale computational workloads. Organizations use Azure's scalable computing resources to perform simulations, data analysis, scientific research, and engineering tasks without the need to maintain expensive on-premises hardware. Azure offers specialized virtual machines, fast networking, and integrated storage to support demanding HPC applications. This allows users to scale resources as needed and only pay for what they use, making high-performance computing more accessible and cost-effective.

What are some typical challenges faced when managing HPC workloads on Azure, and how can they be addressed?

Professionals managing HPC workloads on Azure often encounter challenges related to optimizing resource allocation, managing job scheduling, and ensuring high performance across scalable cloud infrastructure. Addressing these issues typically involves leveraging Azure-native tools like Azure CycleCloud or Batch for automated provisioning and monitoring, as well as carefully selecting VM types and storage options suited for specific workload demands. Regular collaboration with IT, data science, and engineering teams is crucial to ensure that workflows remain efficient, secure, and cost-effective.

What are the key skills and qualifications needed to thrive as an HPC on Azure specialist, and why are they important?

To thrive as an HPC on Azure specialist, you need a strong background in high-performance computing, cloud architectures, and experience with parallel processing frameworks, often supported by degrees in computer science or engineering. Familiarity with Azure services such as Azure Batch, Azure CycleCloud, and networking/security configurations, as well as certifications like Microsoft Certified: Azure Solutions Architect Expert, are highly valuable. Strong problem-solving skills, communication, and adaptability are essential for collaborating with diverse teams and addressing evolving customer requirements. These skills enable the efficient deployment, scaling, and management of HPC workloads on Azure, ensuring optimal performance and cost-effectiveness.
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Hpc On Azure jobs near you
Infographic showing various Hpc On Azure job openings in the United States as of September 2026, with employment types broken down into 93% Full Time, 1% Part Time, and 6% Contract. Highlights an 73% Physical, 8% Hybrid, and 19% Remote job distribution, with an average salary of $121,476 per year, or $58.4 per hour.

Sr Technical Product Manager - HPC Integration

Boston, MA • On-site

QuEra Computing, Inc.
Scientific Research and Development Services • 11 - 50 employees
Learn more about QuEra Computing, Inc.external

$170K - $250K/yr

Full-time

Re-posted 22 days ago

Job description

1. Mission of the Role
To define, validate, and execute the integration strategy for QuEra's quantum systems within classical HPC/AI infrastructure - identifying use cases, technical requirements, and the right ecosystem partners across the full value chain.
2. Core ResponsibilitiesA. Use Case Discovery & Technical Research
  • Deeply study academic and industry papers on quantum-classical hybrid workflows, quantum HPC/AI Data center integration, and the accelerations of AI for Science/Quantum discovery via quantum computing.
  • Identify high-value use cases (e.g., quantum chemistry, materials discovery, cryptoanalysis, optimization) relevant to HPC and AI centers
  • Translate theoretical research into practical product requirements - what integration, APIs, or software layers are needed to run these use cases.
  • Collaborate with research teams to maintain technical credibility when engaging HPC customers or partners.
  • Co-design solutions with lighthouse customers to verify the feasibility

A. Value Chain Mapping
  • Use the HPC value chain framework (hardware → integration → data center → middleware → workloads → services) to:
  • Map current players (AWS, HPE, NVIDIA, etc.) and their quantum readiness.
  • Identify where quantum computing fits within this chain (e.g., as accelerator hardware, a cloud service, or a hybrid scheduler layer).
  • Define which layers QuEra should own vs. partner - e.g.:
  • Own: Quantum hardware, quantum control stack.
  • Partner: Data center operations, cloud orchestration, middleware, hybrid execution APIs.

B. Requirements Definition
  • Gather technical and operational requirements from HPC and AI center operators:
  • Power, cooling, networking, physical integration.
  • Scheduler integration (Slurm, PBS, AWS Batch, Kubernetes).
  • Security, compliance, and monitoring needs.
  • Define product specifications for Quantum-Classical Integration Interfaces (QCII) - the APIs, SDKs, and orchestration layers that enable hybrid workloads.

C. Partner Identification & Engagement
  • Build a partner map for each value chain layer:
  • Hardware partners: compute (NVIDIA, AMD, ARM), interconnect (Infiniband), storage (DDN, AWS FSx).
  • Cloud/HPC providers: AWS, Microsoft Azure, Google Cloud, HPE Cray, NVIDIA DGX Cloud.
  • System Integrators: Dell, HPE
  • Middleware/software partners: Slurm
  • Application/industry partners
  • Lead technical discussions and joint proof-of-concept programs with selected partners.
  • Evaluate integration options - e.g., on-prem quantum node vs. cloud-connected quantum service.

D. Product Strategy & Roadmapping
  • Define a phased integration roadmap for quantum into HPC centers and AI data center:
  • Simulation stage: Run QuEra simulators on HPC clusters.
  • Hybrid orchestration stage: Enable joint scheduling of classical + quantum workloads.
  • Native quantum accelerator stage: Deploy QuEra hardware within HPC/AI center or connect over high-speed link.
  • Balance build/partner decisions at each stage, ensuring scalability and differentiation.

F. Communication & Stakeholder Management
  • Act as the bridge between technical R&D, engineering, business development, and external partners.
  • Create clear documentation (requirements docs, partner briefs, integration specs).
  • Communicate effectively with HPC operators and enterprise customers (translating quantum complexity into operational language).

3. Key Skills and BackgroundTechnical Depth: Background in physics, computer engineering, or computational science; understanding of quantum computing principles and HPC architecture (Slurm, MPI, GPUs, network topology).
Product Management: Experience defining product requirements, roadmaps, and partner strategies in deep tech or HPC environments.
Ecosystem Awareness: Knowledge of cloud and data center ecosystems - AWS, NVIDIA, Intel, HPE, etc.
Cross-Functional Leadership: Ability to collaborate with quantum hardware engineers, software developers, and business teams.
Partnering & Alliances: Experience building technical partnerships and evaluating technology fit.
Communication: Skilled at simplifying deep-technical material for non-specialist executives and partners.
4. Success Metrics
  • A complete map of the HPC/AI ecosystem relevant to quantum integration.
  • Clear technical and operational requirements for deploying QuEra hardware in HPC centers.
  • At least 2-3 strategic technical partnerships (e.g., with AWS, HPE, or NVIDIA).
  • Defined build-vs-partner strategy for each value chain layer.
  • Delivery of hybrid quantum-classical prototype or proof-of-concept within HPC environment.

The approximate base salary range for this position is $170,000-250,000.
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.

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