{"id":3476490,"url":"https://alion.io/job/apple-sr-ai-inference-platform-engineer-2","title":"Sr. AI Inference Platform Engineer","company":{"id":12,"name":"Apple","domain":"apple.com","url":"https://alion.io/company/apple","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Apple Jobs","truth_index":{"grade":"A","score":90,"open_postings":850,"ghost_share":0,"stale_share":0.179,"repost_share":0.002,"time_to_fill_p50_days":93,"computed_at":"2026-11-03T05:54:40Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":227000,"max_usd":437000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1332},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Python","optional":false},{"name":"CI/CD","optional":true},{"name":"Grafana","optional":true},{"name":"Kubernetes","optional":true},{"name":"Prometheus","optional":true},{"name":"Splunk","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"Triton","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-10-29T16:56:32Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-04T00:56:26Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"We are looking for senior engineer to build tooling, automation, and analysis capabilities that strengthen our AI inference platform. This role will focus on developing sophisticated performance benchmarking systems, capacity projection models, and data analysis pipelines that directly inform our AI infrastructure teams and capacity planners. You'll work at the intersection of AI systems performance, distributed infrastructure, and software engineering to help the team make data-driven decisions about scaling and optimizing our inference platform.\nDescription\nAt Apple, we believe the future of AI is defined not just by models, but by the infrastructure that powers them. Our AI inference platform sits at the heart of products and experiences used by hundreds of millions of people worldwide, and we are building the systems that ensure it scales reliably, efficiently, and intelligently.\nAs part of our next-generation datacenter engineering team, you will play a critical role in shaping how we understand, measure, and grow our AI infrastructure. You will design and build the tooling and analysis systems that give our engineers and capacity planners a clear, real-time picture of performance across our fleet. Your work will directly influence how we invest in hardware, how we detect regressions before they reach production, and how we forecast capacity needs months in advance.\nThis is a high-impact, cross-functional role for an engineer who is energized by complexity, thrives on turning raw data into actionable insight, and wants to work on problems that matter at massive scale.\nMinimum Qualifications\nBS or MS in Computer Science or related technical field.\nSolid understanding of AI/ML inference architecture and the performance characteristics of serving systems.\n7 or more years of experience with performance and infrastructure engineering in distributed systems.\n7 years of experience coding in Python, Go, C++, or other programming languages.\nExperience with automation engineering, tooling, and data pipelines to support engineering workflows.\nStrong knowledge of GPU/accelerator architecture as it relates to AI workloads.\nPractical statistical knowledge applicable to performance analysis and forecasting.\nExcellent communication skills and ability to turn data into clear guidance for infrastructure teams and capacity planners.\nPreferred Qualifications\nExperience with performance benchmarking and methodologies for AI/ML inference systems.\nFamiliarity with capacity planning and forecasting/projection models for large-scale infrastructure.\nExperience with GPU profiling and observability tools (e.g., Nsight, other vendor-specific profilers).\nExperience with data visualization and reporting tools/frameworks for surfacing performance trends to stakeholders.\nFamiliarity with ML serving frameworks and runtimes (e.g., Triton, TensorRT-LLM, vLLM, or similar).\nExperience with CI/CD and workflow orchestration tools for building automated performance analysis pipelines.\nKnowledge of cluster schedulers and orchestration platforms (e.g., Kubernetes).\nExperience with metrics and logging tools (e.g., Prometheus, Grafana, 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