NVIDIA is seeking a senior software engineer to help build and scale evaluation and analysis infrastructure for Autonomous Vehicle development. This role sits at the intersection of AV tooling, distributed systems, data platforms, and developer efficiency. You will develop and enhance systems that allow AV engineers to run AV log data analysis, assess model behavior, debug regressions, and produce critical metrics and insights at scale.
This engineer should be able to operate independently, partner deeply with senior technical leads, and bring clarity to complex platform decisions spanning evaluator execution at high-scale, performance tunings, structured outputs and output schema evolution, observability, and production readiness. This role directly improves how AV engineers examine autonomous driving performance, analyze regressions, validate analyzers, and trust the results produced by NVIDIA’s AV infrastructure. The ideal candidate will help make evaluation workflows faster, safer, more observable, and easier for teams to adopt.
What you'll be doing:
- Design and build evaluation and analysis tooling used by AV and ML engineers.
- Improve analyzer workflows, including onboarding, execution, qualification, and production observability.
- Build reliable services, APIs, CI pipelines, and UI-facing workflows across the stack.
- Define and evolve data contracts for analyzer outputs, including structured schemas, tabular data, time-series data, and metadata.
- Improve system stability, performance, traceability, alerting, and operational debuggability for high-scale workloads.
- Partner with AV engineering, data platform, infrastructure, and product teams to deliver end-to-end user workflows.
- Establish guardrails for analyzer rollout, including pre-merge validation, output-size controls, runtime/memory metrics, and production monitoring.
What we need to see:
- BS/MS in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
- 12+ years of software engineering experience building production systems, developer tools, data platforms, or evaluation infrastructure.
- Strong programming skills in Python, Go, or similar languages.
- Experience designing distributed systems, workflow orchestration, APIs, backend services, or data pipelines.
- Ability to reason across systems software, data schemas, backend services, and user-facing tooling.
- Experience collaborating across teams and driving ambiguous technical work to completion.
- Ability to balance new feature development with production stability, maintainability, and user support, while demonstrating agility and adaptability in a fast paced environment.
- Experience with Protobuf, Parquet, schema evolution, data contracts, and large-scale analytical storage.
Ways to stand out from the crowd:
- Background with self-driving car development, simulation, evaluation, or analysis workflows.
- Experience with observability, performance profiling, resource usage analysis, and production reliability.
- Experience improving legacy systems through incremental migration, deprecation, and architecture cleanup.
#LI_Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range 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
- BS or MS in Computer Science, Computer Engineering, Electrical Engineering, a related field, or equivalent experience
- 12 or more years of software engineering experience building production systems, developer tools, data platforms, or evaluation infrastructure
- Strong programming skills in Python, Go, or similar languages
- Experience designing distributed systems, workflow orchestration, APIs, backend services, or data pipelines
- Ability to reason across systems software, data schemas, backend services, and user-facing tooling
- Experience collaborating across teams and driving ambiguous technical work to completion
- Ability to balance feature development with production stability, maintainability, and user support
- Experience with Protobuf, Parquet, schema evolution, data contracts, and large-scale analytical storage
- Background with self-driving car development, simulation, evaluation, or analysis workflows
- Experience with observability, performance profiling, resource usage analysis, and production reliability
- Experience improving legacy systems through incremental migration, deprecation, and architecture cleanup
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.”
