{"id":3979149,"url":"https://alion.io/job/google-staff-software-engineer-ml-compiler-performance-engineer-co-design","title":"Staff Software Engineer, ML Compiler Performance Engineer Co-Design","company":{"id":82,"name":"Google","domain":"google.com","url":"https://alion.io/company/google","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":78,"open_postings":134,"ghost_share":0.045,"stale_share":0.739,"repost_share":0.045,"time_to_fill_p50_days":30,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"GCP","optional":false},{"name":"JAX","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TPU","optional":false},{"name":"Vertex AI","optional":false},{"name":"XLA","optional":false},{"name":"CUDA","optional":true},{"name":"MLIR","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-11-09T14:11:03Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-09T19:07:47Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the job\nGoogle's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.\nWith your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.\nThe MARC (Model Architecture and Realizable-performance Co-design) team is Google’s proactive, ahead-of-silicon co-design engine. We operate at the critical intersection of frontier ML workloads and multi-year TPU silicon roadmaps. Given rapid 6-month model breakthroughs and complex Compound AI Systems, our mandate is to focus on the 12+ month time window between Hardware Architecture Freeze and Physical Silicon Pilot.\nIn this role, you will define and own the strategy for enabling open-source ML models to achieve TPU performance with internal models at launch. You will establish a principled, scalable methodology to analyze mapping gaps across open-source model families.\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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities\nDeliver certified performance benchmarks six months pre-pilot as primary targets for pricing, capacity planning, and kernel optimization.\nAdapt emerging open-source models into TPU-native paired variants, including sparsity optimization, to demonstrate differentiated TPU performance.\nIngest and optimize multi-turn agentic workflows, reasoning loops, and prompt and decode topologies prior to silicon tape-out.\nAuthor robust reference implementations using PyTorch, JAX, or Pallas that meet pre-pilot goals under compiler constraints.\nPartner with director and executive technical leadership across TPU Hardware, XLA Compiler, and Cloud AI Systems to steer multi-year roadmaps.\nQualifications\nMinimum qualifications:\nBachelor's degree or equivalent practical experience.\n8 years of experience programming in C++ or Python.\n5 years of experience testing, and launching software products.\n5 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.\n3 years of experience with software design and architecture.\nPreferred qualifications:\nExperience architecting, validating, or extending high-performance simulation tools, emulation frameworks, or analytical performance modeling pipelines (e.g., roofline models, cycle-accurate, or compiler-aware simulators).\nTechnical proficiency ML workload performance modeling on pre-silicon systems, familiarity with high level compiler intermediate representations (e.g., MLIR dialects, 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