Senior HPC Architect

Senior HPC ArchitectLocation: Warren, NJ (Hybrid)About the RoleWe are seeking a highly specialised Senior GCP 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 GCP.The ideal candidate has personally designed and executed on‑premises HPC to GCP 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 GCP 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 GCP 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 & PlanningLead 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 GCP 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, GCP cost modelling, performance benchmarks, and phased investment roadmaps for sign‑off by research and IT leadership.GCP HPC Architecture for Life SciencesArchitect end-to-end GCP 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 jobsA3 / A2 GPU instances for deep learning genomics workloads (Alpha Fold, Para bricks, deep variant calling)Spot VMs for fault‑tolerant, check pointed pipeline jobs to optimise costDesign 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 Parallel store (Intel DAOS‑based) for high‑throughput scratch and active analysis dataFilestore High Scale / Enterprise for shared pipeline working directoriesCloud Storage with FUSE or XML API for reference genomes, raw sequencing data (FASTQ/BAM/CRAM), and results archivalStorage tiering strategy - active nearline coldline archive - aligned with data…

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