{"id":1935920,"url":"https://alion.io/job/nvidia-performance-engineer-deep-learning","title":"Performance Engineer - Deep Learning","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":"A","score":88,"open_postings":333,"ghost_share":0.015,"stale_share":0.423,"repost_share":0.03,"time_to_fill_p50_days":29,"computed_at":"2026-10-06T05:45:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":124000,"max":195500,"currency":"USD","period":"year","gross":null,"usd_annual":195500},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"cuDNN","optional":false},{"name":"JAX","optional":false},{"name":"LLM","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Transformers","optional":false},{"name":"Triton","optional":false}],"status":"live","first_seen_at":"2026-10-05T23:35:10Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-06T23:41:18Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Our Deep Learning models performance engineering team at NVIDIA is hiring software engineers at all experience levels to build and optimize the libraries and tools that enable Deep Learning Researchers and Engineers to design, develop, and deploy efficient AI applications. We are an ambitious and diverse team that builds optimizations directly into mainstream open source Deep Learning frameworks - PyTorch and JAX, which boost the performance at all levels of NVIDIA's AI stack. Our team has a wide collaborative footprint, working not only with multiple teams across NVIDIA but also with the broader open-source community to deliver SOTA Deep Learning performance on the best AI platform in the world!\nWhat you will be doing:\nBuild and support Transformer Engine, the open-source library for accelerating the training of Large Language Models.\nCollaborate on systems research that improves Deep Learning model performance, such as training using extremely low precision, parallelism methods, etc.\nImplement, benchmark, and optimize new Deep Learning models such as LLMs straight out of groundbreaking research to scale efficiently on NVIDIA GPUs and systems.\nBuild and contribute to NVIDIA submissions on community benchmarks such as MLPerf.\nEngage with the open-source community as well as support enterprise customers and partners by delivering the benefits of NVIDIA’s latest hardware and software innovations.\nInfluence the design of new hardware generations and core platform software components for NVIDIA hardware and systems.\nWhat we need to see:\nBS or equivalent experience in Computer Science, Electrical Engineering, or a related field.\n2+ years of experience in C++ and Python programming.\nStrong background, experience, or coursework in parallel systems programming, preferably on GPUs.\nKnowledge of Computer Architecture, Code Optimization, and/or Operating Systems.\nProven experience in developing large software projects.\nExcellent verbal and written communication skills.\nWays to stand out from the crowd:\nExperience in PyTorch, JAX, or any other DL framework.\nExperience with performance analysis, profiling, and code optimization techniques, especially with multi-GPU or multi-node systems.\nKnowledge of modern LLM architectures, attention mechanisms, and/or low-level DL libraries such as cuBLAS, cuDNN, and cuSOLVER.\nExperience in writing GPU kernels using any of - CUDA, OpenAI Triton, CuTeDSL, Pallas, or other similar libraries.\nAny past contributions to the open source community and/or experience working with multidisciplinary teams also showcase readiness for the team's responsibilities. \nYour base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3.You will also be eligible for equity and benefits.\nApplications for this job will be accepted at least until October 9, 2026.This posting is for an existing vacancy.\nNVIDIA uses AI tools in its recruiting processes.\nNVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. 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