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. Today, 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 an inclusive, 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.
NVIDIA is seeking a Software Engineer, Data Systems to join our Santa Clara, CA team. This opportunity lets you work with innovative technology that changes how we analyze AI workload performance across GPU data centers. Our team builds advanced software tools to help technical users understand complex computing systems. You will manage the store, data model, and query path, converting large streams of profiling and telemetry data into useful information for engineers and automated analysis.
What you’ll be doing:
- Develop and construct the store, data model, and query path for engineering performance data. This includes ingesting, joining, storing, and delivering it at the scale of a GPU data center with low latency.
- Join profiling data with context from the rest of the system, such as hosts, schedulers, configuration, logs, and storage, ensuring each record carries the necessary identity and dimensions for comparison across jobs, machines, versions, and time.
- Take responsibility for the data contract with the teams that generate the data: establish it, version it, and transition consumers through changes without disrupting them.
- Make deliberate trade-offs among data quality, query latency, storage cost, and retention, and verify improvements in efficiency, latency, and resource use with measurements.
- Handle delayed, duplicated, incomplete, or inconsistent data, and make its limitations visible to downstream users.
- Develop the data API along with the generated evidence views for people and automated analysis to access results. Implement access controls, resource limits, and maintain stable behavior during high demand or failures.
What we need to see:
- Bachelor’s degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
- 12+ years of relevant software engineering experience, including delivery and operation of data pipelines, storage systems, analytical services, or comparable software used by others.
- Experience handling large-scale data in production, including billions of events per day or petabyte-class stores. Also experienced with low-latency streaming pipelines that manage backpressure, replay, and duplicate delivery accurately.
- Good judgment about event identity and ordering, data transformations, and how errors or missing data affect results.
- A concrete performance investigation you can explain: the baseline, measurements, competing explanations, change made, and verified result, including trade-offs.
- Experience establishing and versioning a data contract with a producing team, and guiding consumers through a format change.
- Ability to debug across component boundaries and validate changes through code review, tests, and independent measurements, including when using automated development tools.
- Clear communication about choices in building, observed behavior, and uncertainty, with care for users’ data and operational constraints.
- Proficiency with C++, Python, and SQL.
Ways to stand out from the crowd:
- Hands-on work with telemetry, time-series data, or analytical query engines such as ClickHouse, and with columnar formats such as Arrow and Parquet.
- Familiarity with profiler data: trace record formats, sampling semantics, symbolization, clock domains, and aligning events recorded on different machines.
- High-cardinality schema build, and measured improvements to memory use, storage efficiency, or query performance while preserving correctness.
- A track record of making data processing easier to inspect and recover when inputs change, traffic spikes, or failures occur.
- Working knowledge of Go.
Join us at NVIDIA and be part of a team that is defining the next era of computing. Together, we can make a significant impact on the world!
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 224,000 USD – 356,500 USD.You will also be eligible for equity and benefits.
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.Skills Required
- Bachelor’s degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
- 12+ years of relevant software engineering experience.
- Experience delivering and operating data pipelines, storage systems, analytical services, or comparable software used by others.
- Experience handling large-scale production data, including billions of events per day or petabyte-class stores.
- Experience with low-latency streaming pipelines involving backpressure, replay, and accurate duplicate delivery handling.
- Strong judgment regarding event identity and ordering, data transformations, and the effects of errors or missing data on results.
- Ability to explain a concrete performance investigation, including baseline, measurements, competing explanations, changes, verified results, and trade-offs.
- Experience establishing and versioning data contracts with producing teams and guiding consumers through format changes.
- Ability to debug across component boundaries and validate changes through code review, testing, and independent measurement.
- Clear communication about technical choices, observed behavior, uncertainty, user data, and operational constraints.
- Proficiency with C++, Python, and SQL.
- Hands-on experience with telemetry, time-series data, analytical query engines such as ClickHouse, or columnar formats such as Arrow and Parquet.
- Familiarity with profiler data, trace record formats, sampling semantics, symbolization, clock domains, and cross-machine event alignment.
- Experience building high-cardinality schemas and improving memory use, storage efficiency, or query performance while preserving correctness.
- Track record of making data processing easier to inspect and recover during input changes, traffic spikes, or failures.
- Working knowledge of Go.
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 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, NVIDIA is increasingly known as “the AI computing company.”
