Demonstrate and integrate NVIDIA's data processing stack (libraries, SDKs, and tools) into real query engines and OLAP databases, carrying the work from prototype through to a functioning, measured integration across cloud, hybrid, and on-prem deployments.
Support developers, projects, and partners through onboarding and integration by providing working reference implementations, integration guides, and direct hands-on engineering help, so that a first integration produces a real accelerated query path rather than a demo.
Track the analytical data processing ecosystem: new engines, execution models, storage formats, and competing approaches to acceleration. Share what you learn with NVIDIA engineering, product, marketing, and the worldwide field organization to shape adoption strategy.
Collaborate with engine architects and NVIDIA engineering to resolve integration problems, establish best-practice patterns, and feed concrete technical requirements back to NVIDIA product teams.
Define the integration surface between NVIDIA's GPU data processing libraries and third-party query engines: where the plan handoff occurs, how execution and memory ownership are divided, and which of a partner's differentiating components remain in place alongside ours. Specify the APIs NVIDIA must expose to make that possible, and carry the patterns that work forward from one integration to the next.
Own the benchmark evidence for GPU-accelerated analytics. Design and run TPC-H, TPC-DS, and ClickBench measurements against the strongest available CPU baselines, separate cold and warm behavior, and publish results that hold up to outside scrutiny. Use those results to establish where the acceleration case is proven, where it is not yet, and what has to change.
Own the acceleration roadmap jointly with the projects and partners you work with: what gets built, in what order, and which technical bets are worth making. Earn that standing where the work happens, through upstream contribution and public design review in open source, and through joint architecture and roadmap planning with partner engineering leadership.
What We Need to See
Bachelor's or Master's degree or equivalent experience in Computer Science, Engineering, or a related field.
6+ years of overall professional experience in the technology industry in software engineering, developer relations, technical partnerships, solutions architecture, or product management, including hands-on experience with analytical data systems. Equivalent evidence of domain authority is weighed in place of years: published systems research, maintainership of a widely used data system, or core contributions to a query engine or data processing library.
Experience working with or supporting open source data projects and their contributor communities, commercial data platform and database ISVs, or cloud service provider data services.
Working proficiency in the internals of analytical data systems: query execution and optimization, vectorized and columnar processing, joins and aggregation, and storage formats such as Parquet and Arrow. Comfortable reading and contributing to a large C++, Rust, or Python codebase.
Comfort collaborating with cross-functional teams to discuss architecture, share feedback, and deliver technical presentations or demos.
Ability to manage and implement technical projects, solve integration challenges, and effectively communicate complex ideas to both technical and non-technical audiences.
Strong communication skills and a passion for helping developers innovate with NVIDIA tools and technology.
Ways to Stand Out from the Crowd
Committer, maintainer, or sustained contributor to a widely used open source data system..
Shipped an acceleration layer or engine integration into a commercial data platform, end to end.
Published or presented systems work at venues such as VLDB, SIGMOD, CIDR, or major open source community conferences..
Experience serving as technical counterpart to partner engineering leadership, including architecture review, design review, and joint roadmap planning.
Hands-on familiarity with advanced computing and GPU acceleration platforms, including CUDA, RAPIDS, cuDF, nvCOMP, and related CUDA-X libraries.
With competitive salaries and a generous benefits package, we are widely considered to be one of the 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 outstanding growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous person with a real passion for technology, we want to hear from you.
- Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
- 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
