{"id":2055234,"url":"https://alion.io/job/google-customer-engineer-i-ai-infrastructure-google-cloud","title":"Customer Engineer I, AI Infrastructure, Google Cloud","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":"Solutions","role_family":"Solutions","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":96000,"max_usd":225000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":282},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"TPU","optional":false},{"name":"Fine-tuning","optional":true},{"name":"Kubernetes","optional":true},{"name":"PyTorch","optional":true},{"name":"TensorBoard","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-07T22:09:04Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-12T11:11:43Z","board_verified":true,"closed_at":null,"days_open":4,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":4},"description":"About the job\nWhen leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.\nAs a Practice Customer Engineer (CE) specializing in AI Infrastructure, you will partner with technical Sales teams to incubate, pilot, and deploy Google Cloud’s industry-leading AI/ML accelerators (TPU/GPU) for AI startups and large enterprises. You will help customers innovate faster using Google Cloud’s flexible, open infrastructure.\nIn this role, you will identify large-scale AI opportunities and integrate accelerators into customers’ cloud strategies by running model benchmarks, developing migration paths, and analyzing cost-to-performance. Partnering with Platform CEs, you will write code to build prototypes, proofs-of-concept, and demos that solve complex AI challenges and influence product development.\nYou will gather business and technical requirements to persuasively present practical solutions that prove the value of the Google Cloud portfolio.\nGoogle Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.\nIndividual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $104000 - $150000 (USD) + 42.86% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities\nBea trusted advisor to our customers, helping them understand and incorporate AI accelerators into their overall cloud strategy by recommending migration paths, integration strategies, and application architecture that incorporate Google Cloud AI optimized infrastructure.\nDemonstrate how Google Cloud is differentiated, highlighting the power of accelerators by working with customers on proof of concepts, demonstrating features, optimizing model performance, profiling, and benchmarking.\nBuild repeatable assets to enable other customers and internal teams.\nInfluence Google Cloud strategy at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements.\nTravel to customer sites and events as needed.\nQualifications\nMinimum qualifications:\n Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.\n4 years of experience with cloud infrastructure.\nExperience building and operationalizing machine learning models.\nExperience in delivering technical presentations, leading discovery and planning sessions.\nPreferred qualifications:\nExperience training and fine tuning large models (i.e., image, language, segmentation, recommendation, genomics) with accelerators.\nExperience with performance profiling tools (i.e., TensorFlow profiler, PyTorch profiler, Tensorboard).\nExperience designing/architecting large-scale infrastructure farms for specialist AI use cases.\nExperience with containerization, Kubernetes, Kubernetes on Google Cloud.\nExperience with machine learning 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