{"id":1917091,"url":"https://alion.io/job/nvidia-principal-software-engineer-distributed-query-engines-analytics-and-data-intelligence","title":"Principal Software Engineer, Distributed Query Engines - Analytics and Data Intelligence","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":"Backend","role_family":"Backend","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Shanghai, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":54000,"max_usd":149000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1532},"experience_years_min":15,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"cuDF","optional":false},{"name":"ETL/ELT","optional":false},{"name":"RAPIDS","optional":false}],"status":"live","first_seen_at":"2026-10-05T16:43:05Z","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":"NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology-and amazing people.\nToday, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.\nWe're looking for outstanding engineers and scientists to join NVIDIA’s Analytics and Data Intelligence (ADI) team. This is an outstanding chance to employ your parallel programming skills to accelerate open-source software libraries for GPU data processing. You will have the opportunity to support high-performance structured data processing on hardware from single workstations to rack-scale GPU supercomputers. You will contribute significantly to building the computational core for dataframe and database accelerators by developing highly optimized C++ and CUDA libraries. These libraries use the parallel power of GPUs to speed up tasks such as data loading, parsing, joins, aggregations, and more. Bring your creativity and problem-solving skills to our open-source software suite, and become our next major contributor!\nWhat you’ll be doing:\nCreating and improving C++ and CUDA libraries for high-performance data processing, including end-to-end data paths from storage to GPU compute engines\nAccelerating operations such as data loading, parsing, joins, aggregations, as well as I/O, data movement, memory management, and concurrency optimization\nCollaborating with the Sales/DevRel team to support partner integration, PoCs, performance tuning, and production issue resolution with partners in China\nImplementing highly performant solutions for warehouse and Lakehouse workloads across compute, networking, and distributed storage\nWorking with a globally distributed and highly technical team to drive software initiatives from architecture to delivery\nApplying deep experience with data analytics and ETL ecosystems to optimize GPU-accelerated data processing pipelines\nWhat we need to see:\nBachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent experience in practice\n15+ years of relevant software engineering experience, including substantial work in systems software, distributed data platforms, storage, databases, or accelerated computing\nProven proficiency in C++ and CUDA programming\nStrong knowledge of data processing, data analytics, and storage environments\nDeep expertise in at least one of the following: distributed storage, database/query engine internals, high-performance I/O, accelerated computing, or large-scale data platforms\nExperience driving efficient I/O and data-movement paths across distributed/object storage, table formats (e.g., Iceberg, Parquet), Lakehouse architectures, and analytical engines\nFamiliarity with, or contributions to, SiriusDB, GPU-accelerated query engines, or related projects\nWays to stand out from the crowd:\nFamiliarity with RAPIDS cuDF\nFamiliarity with S3, Parquet, and Iceberg\nExperience with cross-stack performance optimization spanning storage, networking, and CPU/GPU compute\nProven ability to diagnose and resolve production issues in complex distributed systems\nAbility to both define technical direction and personally implement critical components while driving cross-team delivery\nWidely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. 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