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Google Hpc Jobs (NOW HIRING)

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HPC Engineer, Services

$100K - $150K/yr

Interest in cloud computing platforms such as AWS, Azure or Google Cloud * General awareness of HPC, schedulers, MPI, containers, kubernetes, and GPU computing * Introductory exposure to scientific ...

Rescale

Senior HPC Engineer, Services

$100K - $150K/yr

Familiarity with cloud computing platforms such as AWS, Azure or Google Cloud * High-level knowledge of HPC, schedulers, MPI, containers, kubernetes, and GPU computing * Familiarity with scientific ...

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How much do google hpc jobs pay per hour?

As of Jun 26, 2026, the average hourly pay for google hpc in the United States is $27.93, according to ZipRecruiter salary data. Most workers in this role earn between $20.67 and $33.65 per hour, depending on experience, location, and employer.

What is Google HPC?

Google HPC (High Performance Computing) refers to Google's infrastructure and services that support large-scale computational tasks, often involving specialized hardware, parallel processing, and cloud-based resources. Jobs in this field typically require knowledge of distributed systems, programming skills, and familiarity with tools like Kubernetes or TensorFlow.

What is L1, L2, L3, and L4 in Google?

In the context of a Google HPC (High Performance Computing) environment, L1, L2, L3, and L4 typically refer to different levels of cache memory hierarchy or job priority levels. L1 cache is the fastest and closest to the processor cores, while L2 and L3 are larger but slightly slower caches. L4, when used, often denotes an even larger cache or memory level, or a specific tier in resource allocation, depending on the system architecture. Understanding these levels helps optimize computational performance and resource management in high-performance jobs.

What is a Google HPC engineer?

A Google HPC (High Performance Computing) engineer is a professional who designs, implements, and manages high-performance computing solutions using Google Cloud technologies. Their responsibilities include optimizing cloud infrastructure for computationally intensive workloads, supporting scientific and engineering applications, and ensuring efficient use of resources. These engineers work with clusters, parallel computing, and specialized hardware to enable large-scale data processing and advanced research. They collaborate closely with research teams, software developers, and IT specialists to deliver scalable and reliable HPC solutions.

What are the key skills and qualifications needed to thrive as a Google HPC (High Performance Computing) Engineer, and why are they important?

To thrive as a Google HPC Engineer, you need a strong background in computer science, parallel computing, and large-scale system architecture, often supported by a relevant degree and experience with HPC workloads. Familiarity with cloud platforms (like Google Cloud Platform), job schedulers (Slurm, PBS), and programming languages (Python, C/C++, MPI) is typically required, along with relevant certifications. Excellent problem-solving, collaboration, and communication skills help you work efficiently with cross-functional teams and clients. These skills and qualities are vital to optimize performance, ensure system reliability, and deliver scalable computing solutions for complex workloads.

What are the typical collaboration opportunities for someone working in a Google HPC (High Performance Computing) role?

Professionals in a Google HPC role frequently collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to design and optimize scalable computing solutions. You’ll often work closely with researchers to understand their computational needs and with cloud infrastructure teams to ensure efficient resource allocation. This collaborative environment helps you stay at the forefront of technological advances, while also allowing you to develop expertise in both technical and interpersonal skills. Regular interactions with other teams foster innovation and provide ample learning opportunities.

Which job is highly paid in Google?

In Google, senior roles such as Staff Software Engineer, Engineering Manager, and Technical Lead tend to be the highest paid positions, often earning six-figure salaries plus bonuses and stock options. These roles require extensive experience, advanced technical skills, and leadership capabilities. Compensation varies based on location, experience, and performance.

What is the future of HPC?

The future of high-performance computing (HPC) involves increasing computational power through advancements in hardware such as GPUs and specialized processors, as well as software optimization for parallel processing. HPC professionals will need skills in programming, system architecture, and data management to support applications in scientific research, AI, and big data analytics. As demand grows, roles in HPC are expected to expand across industries requiring large-scale data processing and simulation capabilities.
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Google Hpc jobs near you
Infographic showing various Google Hpc job openings in the United States as of June 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 33% In-person, and 67% Remote job distribution, with an average salary of $58,088 per year, or $27.9 per hour.

Senior HPC Architect

TESTINGXPERTS, INC. DBA DAMCOSOFT

Warren, NJ • On-site

Other

Posted 2 days ago

Job description

Senior HPC Architect

Location: Warren, NJ (Hybrid)

About the Role

We are seeking a highly specialised Senior Google Cloud Platform Architect with deep, hands-on High Performance Computing (HPC) expertise in Life Sciences and Genomics to lead the migration of large-scale on-premises HPC environments to Google Cloud Platform. This is a hands-on technical leadership role - not an advisory position - requiring genuine experience migrating production HPC infrastructure and scientific computing workloads from on-premises clusters to Google Cloud Platform.

The ideal candidate has personally designed and executed on-premises HPC to Google Cloud Platform migration programmes in life sciences or genomics settings, understands the unique regulatory, data sensitivity, and performance demands of genomic pipelines and scientific workloads, and can architect Google Cloud Platform HPC environments that match or exceed the performance of the on-premises systems being replaced.

You will work directly with research computing teams, bioinformatics leads, IT infrastructure staff, and senior client stakeholders - translating complex scientific computing requirements into well-architected, compliant, and cost-optimised Google Cloud Platform solutions. This role requires someone equally comfortable debugging a GATK pipeline performance issue with a bioinformatician and presenting a cloud HPC migration business case to a CIO.

Key Responsibilities

HPC Migration - Discovery & Planning

  • Lead end-to-end on-premises HPC migration discovery for life sciences and genomics environments - including full inventory of existing clusters, scheduler configurations, storage systems, application stacks, and data assets.
  • Conduct structured discovery workshops with research computing teams, bioinformatics leads, laboratory IT staff, and HPC administrators to document current-state architecture, workload profiles, job scheduling patterns, and pain points.
  • Perform detailed workload characterisation - profiling genomic pipeline jobs (WGS, WES, RNA-seq, single-cell, variant calling) across compute, memory, storage I/O, and runtime dimensions to inform Google Cloud Platform sizing and architecture decisions.
  • Build comprehensive application and dependency maps - cataloguing HPC software stacks (bioinformatics tools, pipeline frameworks, commercial ISV applications), license dependencies, data dependencies, and inter-workload relationships.
  • Develop HPC Migration Readiness Assessments (MRA) - evaluating gaps in network connectivity, data transfer capacity, security and compliance posture, team cloud readiness, and pipeline portability before migration begins.
  • Define migration wave plans sequencing workload migration based on complexity, scientific criticality, data volumes, regulatory sensitivity, and dependency chains - enabling a phased, low-risk transition.
  • Build detailed migration business cases including on-premises TCO analysis, Google Cloud Platform cost modelling, performance benchmarks, and phased investment roadmaps for sign-off by research and IT leadership.

Google Cloud Platform HPC Architecture for Life Sciences

  • Architect end-to-end Google Cloud Platform HPC environments optimised for genomics and life sciences workloads, leveraging Google Cloud's HPC-specific compute, networking, storage, and managed services.
  • Select and right-size compute instance families for life sciences HPC workloads:
    • C3 / N2 instances for CPU-intensive bioinformatics tools (BWA, GATK, STAR, Salmon)
    • M3 / M2 memory-optimised instances for large in-memory genomics jobs
    • A3 / A2 GPU instances for deep learning genomics workloads (AlphaFold, Parabricks, deep variant calling)
    • Spot VMs for fault-tolerant, checkpointed pipeline jobs to optimise cost
  • Design low-latency cluster networking using compact placement policies, Google's RDMA-capable networking, and GPUDirect RDMA for tightly coupled parallel workloads.
  • Architect high-performance parallel storage solutions for genomics data:
    • Google Parallelstore (Intel DAOS-based) for high-throughput scratch and active analysis data
    • Filestore High Scale / Enterprise for shared pipeline working directories
    • Cloud Storage with FUSE or XML API for reference genomes, raw sequencing data (FASTQ/BAM/CRAM), and results archival
    • Storage tiering strategy - active nearline coldline archive - aligned with data lifecycle and access patterns
  • Design Slurm-based HPC cluster architectures on Google Cloud Platform using Google Cloud HPC Toolkit, including:
    • Auto-scaling partition configuration for variable genomics job demand
    • Reservation management for predictable sustained workloads
    • Preemptible/Spot VM integration for cost-optimised burst capacity
    • Multi-partition designs separating short-job, long-job, GPU, and high-memory workloads
  • Implement Google Batch for high-throughput genomics pipelines requiring parallel task execution across thousands of samples.

Experience

  • Overall experience in cloud infrastructure, HPC, or research computing.
  • hands-on Google Cloud Platform architecture with deep command of Google Cloud Platform compute, networking, storage, and life sciences services.
  • hands-on HPC experience in life sciences or genomics - designing, operating, and migrating HPC clusters supporting bioinformatics or research computing workloads. Production experience required.
  • Demonstrated experience leading on-premises HPC to Google Cloud Platform migration programmes - from discovery through cutover and decommission - at research or enterprise scale.
  • Hands-on experience with genomics pipeline frameworks - WDL/Cromwell, Nextflow, Snakemake, or equivalent - and their deployment on Google Cloud Platform execution backends.
  • Hands-on experience with Google Cloud HPC Toolkit for Slurm cluster deployment, configuration, and customisation on Google Cloud Platform.
  • Proven experience with large-scale genomics data migration - petabyte-scale FASTQ/BAM/VCF datasets - using Storage Transfer Service, Transfer Appliance, or equivalent.
  • Experience with Slurm workload manager - cluster configuration, partition design, job scheduling, and migration from PBS/Torque/LSF environments.
  • Strong command of Google Cloud Platform storage architecture for HPC - Parallelstore, Filestore, Cloud Storage, and storage tiering for genomics data lifecycle management.
  • Experience with HIPAA-compliant cloud architecture for genomics or clinical data environments.
  • Strong Linux systems administration at the HPC level - kernel tuning, environment modules, cluster OS image management, and MPI environment configuration.

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