NVIDIA's Developer Tools team is seeking a Senior Backend Platform Engineer to build scalable and reliable capabilities for profiling services. This role combines production backend engineering with data-intensive systems work. You will improve how complex performance data is processed, served, and operated. The work involves close collaboration with partners in frontend, security, product, and source systems.
What you’ll be doing:
Design and build production backend services for interactive performance analysis and collaborative workflows.
Improve the responsiveness, scalability, and efficiency of data-intensive product experiences.
Develop reliable capabilities for onboarding, validating, organizing, and serving large performance datasets.
Create durable service interfaces and data models that can evolve as product needs grow.
Engineer resilient behavior for concurrency, partial failures, retries, and recovery.
Establish effective testing, observability, and operational practices for the capabilities you own.
Partner with frontend, security, product, and source-system teams to deliver complete customer workflows.
What we need to see:
BS or MS in Computer Science, Data Engineering, or a related field, or equivalent experience.
5+ years of experience building production backend services, distributed systems, analytical systems, or data platforms using Python or a comparable language.
Strong software engineering fundamentals, including automated tests, failure handling, production diagnostics, and compatible evolution of APIs or stored data.
For service-focused work, experience with asynchronous backends and hands-on depth in query execution or real-time stateful systems.
For data-focused work, strong SQL and experience with ingestion, schema evolution, analytical storage, or query-optimized data modeling.
Sound judgment about failure and concurrency, demonstrated through problems such as cancellation, replay, safe retries, or recovery.
Ability to measure system behavior and turn logs, metrics, traces, load tests, or data-quality signals into practical improvements.
A record of working across client, service, data, and security concerns to ship a complete production workflow.
Ways to stand out from the crowd:
Practical coding skills in Python, C++, or Rust, encompassing the capability to write, review, and direct production-quality infrastructure software. Experience with Rust is highly valued.
Experience making high-cardinality, time-indexed, trace, telemetry, or profiling workloads responsive at scale.
Experience keeping live state consistent with WebSockets, pub/sub, replay, or comparable real-time techniques.
Experience improving analytical reads or ingestion with columnar formats, embedded query engines, analytical stores, progressive detail, or incremental processing.
Background in generated clients, secure sharing, authorization-aware caching, provenance, lifecycle automation, or cost attribution.
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
- BS or MS in Computer Science, Data Engineering, or related field, or equivalent experience.
- 5+ years building production backend services, distributed systems, analytical systems, or data platforms.
- Proficiency in Python or a comparable language (C++ or Rust experience valued).
- Strong software engineering fundamentals: automated tests, failure handling, production diagnostics, API/data evolution.
- Experience with asynchronous backends and depth in query execution or real-time stateful systems.
- Strong SQL and experience with ingestion, schema evolution, analytical storage, or query-optimized data modeling.
- Sound judgment about failure and concurrency (cancellation, replay, safe retries, recovery).
- Ability to measure system behavior and use logs, metrics, traces, load tests, or data-quality signals to improve systems.
- Proven track record working across client, service, data, and security domains to ship complete production workflows.
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 — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
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.”
