{"id":3536758,"url":"https://alion.io/job/nvidia-deep-learning-compiler-ci-and-infrastructure-engineer","title":"Deep Learning Compiler CI and Infrastructure Engineer","company":{"id":6,"name":"NVIDIA","domain":"nvidia.com","url":"https://alion.io/company/nvidia","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":806,"ghost_share":0.024,"stale_share":0.568,"repost_share":0.048,"time_to_fill_p50_days":40,"computed_at":"2026-11-05T05:54:30Z"}},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Shenzhen, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":43000,"max_usd":102000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"GitLab CI","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"MLIR","optional":false},{"name":"Python","optional":false},{"name":"SLURM","optional":false},{"name":"TensorRT","optional":false}],"status":"live","first_seen_at":"2026-10-30T17:53:20Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-11-05T13:01:38Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"NVIDIA's invention of the GPU 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI - the next era of computing - with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company”.\nIn this role you will work closely with deep learning compiler engineers to own and evolve the CI/CD infrastructure that powers the development lifecycle of NVIDIA's deep learning compiler stacks. Responsibilities include designing and operating scalable CI systems that orchestrate ML workloads across diverse GPU and accelerator environments, deliver reliable correctness and performance signals, and serve as a primary technical point of contact for CI health, new project onboarding, and new architecture bring-up.\nWhat you'll be doing:\nBuild, maintain, and improve CI infrastructure that supports development, verification, and release of NVIDIA’s deep learning compiler stacks across GPU and accelerator environments\nImprove CI reliability and signal quality by reducing flakes, improving reproducibility, strengthening diagnostics, and making correctness and performance failures easier to understand and act on\nApply automation, AI, and agent-based workflows to reduce manual CI operations, speed up failure triage, and improve developer efficiency\nBuild reusable and self-service CI platforms that support multiple products, projects, model suites, hardware targets, and software configurations while partnering closely with compiler, infrastructure, and release teams\nWhat we need to see:\nBS, MS, or PhD (or equivalent experience) in Computer Science, Computer/Electrical Engineering, Mathematics, or a related field\n5+ years of experience designing, scaling, and operating CI/CD, build/release, or developer infrastructure for complex software systems\nProven experience building CI platforms end-to-end using systems such as GitLab CI, Jenkins, or similar tools, including pipeline orchestration, compute/runner management, artifact and package systems, and observability, with strong emphasis on reliability, reproducibility, and debuggability\nStrong software engineering skills (Python required), with the ability to design, implement, and debug distributed systems end-to-end\nFamiliarity with edge devices (SOC, e.g. NVIDIA Tegra) in host-target architecture, ability to debug it and knowledge to its automation nuances\nProven track record of designing, building, and deploying AI/LLM-based systems in real engineering workflows, demonstrating skill in evaluating trade-offs, failure modes, maintainability, and measurable impact on developer productivity, signal quality, or operational efficiency\nWays to stand out from the crowd:\nExperience crafting and shipping sophisticated AI/agent-based systems that improve continuous integration or developer efficiency. These systems include intelligent test selection, automated triage and routing, regression localization, autonomous remediation, and developer-assist workflows\nExperience operating CI for DL/GPU software environments, including multi-GPU / multi-node workloads on Slurm, Kubernetes, or cloud platforms\nFamiliarity with compiler IRs and infrastructure such as LLVM/MLIR, TensorRT IR, especially in the context of testing, debugging, and validating compiler-driven workloads\nWith competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. 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