{"id":2048355,"url":"https://alion.io/job/google-principal-engineer-google-compute-engine-specialized-instances-2","title":"Principal Engineer, Google Compute Engine, Specialized Instances","company":{"id":82,"name":"Google","domain":"google.com","url":"https://alion.io/company/google","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":75,"open_postings":103,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-10-12T06:55:09Z"}},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seattle, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":158000,"max_usd":320000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":988},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Edge AI","optional":false},{"name":"GCP","optional":false},{"name":"HPC","optional":false},{"name":"Redis","optional":false},{"name":"SAP HANA","optional":false},{"name":"Spark","optional":false},{"name":"TPU","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-10-07T19:39:08Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-12T17:12:39Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"About the job\nAs the Principle Engineer for Specialized Instances, you will establish workload-optimized portfolio roadmap by defining GCE instances that meet the stringent resource, reliability, and performance demands of large-scale AI/ML clusters and mission-critical applications. These instances are in the category of Storage, Networking, and Memory Optimized, as well as HPC.\nOur workload-optimized portfolio also includes memory and storage-optimized instances. Storage-optimized instances are ideal for data-intensive applications like high-performance databases (e.g., SAP HANA), data warehousing, and large-scale media processing, where rapid access to vast datasets is essential. Memory-optimized instances exceed at running in-memory databases (e.g. Redis) and real-time analytics platforms (e.g. Apache Spark).\nIn this role, you'll also manage the challenge of providing low-latency, high-throughput storage solutions essential for the exponential growth of AI/ML clusters. This includes optimizing for applications that require rapid access to training data, checkpointing, and model serving, where local SSDs and storage-optimized VMs are critical for achieving optimal performance.\nAdditionally, you will drive technical innovation to push the boundaries of performance, reliability, and cost-efficiency across all these workloads, ensuring our customers have a seamless and exceptional experience. This is an opportunity to shape the future of cloud computing by directly impacting the core compute offerings of Google Cloud Platform.\nThe AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.\nWe're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.\n Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $307000 - $427000 (USD) + 30% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities\nEstablish industry-leading workload-optimized portfolio roadmap by defining GCE instances that meet the stringent resource, reliability, and performance demands of large-scale AI/ML clusters and mission-critical applications.\nLead the design, development, and optimization of GCE instances tailored to the unique performance and reliability needs of unique workloads, including collaborating with internal teams and Google’s hardware architects to ensure optimal performance and 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