Senior System Software Engineer— Autonomous Driving Context Fusion
NVIDIA
Opportunity published from Workday · applications stay with the original hiring source.
About the opportunity
We are looking for a Software Optimization Engineer to join our DRIVE Road Structure / Online Mapping / Context Fusion team.
In this role, you will improve the efficiency and real-time performance of software that brings together perception, map, and localization data to build a consistent model of the driving environment.
You will work closely with algorithm and platform engineers to identify bottlenecks, optimize software architecture and data flows, and deliver measurable improvements in CPU utilization, memory usage, and latency on automotive platforms.
Your work will help advanced autonomous driving capabilities run reliably within demanding compute and timing constraints.
Key responsibilities
- Optimize context fusion software that integrates perception, map, and localization data to represent the driving environment.
- Design and refine software architecture, interfaces, and data flows to meet CPU, memory, and latency requirements on automotive platforms.
- Profile complex software pipelines, identify performance bottlenecks, and implement improvements backed by reproducible measurements.
- Work with algorithm teams to improve computational efficiency while preserving algorithm behavior, output quality, and correctness.
- Develop benchmarks and performance regression tests to track improvements and prevent regressions.
- Partner with perception, mapping, localization, and platform engineers to resolve system-level performance issues.
- Write reliable, maintainable code and contribute to code reviews, testing, and the safety and quality expectations of autonomous driving software.
Qualifications
- A PhD with 4+ years, an MS with 6+ years, or a BS (or equivalent experience) with 8+ years of relevant experience in Computer Science, Computer Engineering, or a related technical field.
- Strong proficiency in modern C++ and extensive experience designing and developing performance-sensitive software.
- Demonstrated ability to profile and optimize CPU utilization, memory consumption, and end-to-end latency.
- A strong understanding of data structures, algorithms, memory management, concurrency, and their impact on software performance.
- Experience using profiling tools and benchmarks to diagnose bottlenecks and validate improvements under representative workloads.
- A systematic approach to debugging and optimization, with a focus on preserving correctness and reliability.
- Strong communication skills and the ability to collaborate effectively across algorithm, software, and platform teams.
Nice to have
- Experience optimizing real-time software on embedded or automotive platforms.
- Familiarity with context fusion, map fusion, localization, perception pipelines, state estimation, or related ADAS and autonomous driving systems.
- Experience with cache-aware data layouts, memory allocation optimization, multithreading, or SIMD/vectorization.
- Experience designing efficient APIs and reusable software frameworks for complex systems.
- A track record of delivering measurable performance improvements in production software while preserving correctness, output quality, and reliability.
Benefits
- Base salary determined based on location, experience, and other objective job-related factors (184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5).
- Equity eligibility.
- Comprehensive benefits package.
Job details
- Applications accepted at least until November 7, 2026.
- This posting is for an existing vacancy.
About the company
NVIDIA is a leader in advancing computing for AI, autonomous vehicles, and robotics. Our technologies empower developers and engineers to solve complex computational challenges across industries.
Additional information
- NVIDIA uses AI tools in its recruiting processes.
Application: UpVagas lists this opportunity from the hiring company or its recruitment platform. The application is completed at the original source.
