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Design and implement high-throughput, low-latency logging frameworks capable of handling massively parallel data streams
Define smart, dynamic data collection policies (e.g., triggering high-fidelity snapshots during performance events while intelligently downsampling routine driving data)
Evaluate, select, and tune file systems to maximize write speeds and guarantee data integrity
Collaborate with systems and hardware teams to select storage media (NVMe, SSDs) and optimize kernel-level I/O performance
Work with autonomy engineers to meet performance and efficiency requirements for data collection and offload
Provide engineering support for field testing and fleet operations
Mentor senior engineers, set coding standards, and drive the long-term roadmap for on-vehicle data lifecycle management
Requirements
BS/MS/PhD in Computer Science or related field, or equivalent industry experience
Expert-level C++ programming skills and the ability to design high-performance, thread-safe, and memory-efficient systems in a Linux environment
Proven experience working with storage technologies, block devices, and making informed file system choices based on write endurance, throughput, and crash-consistency constraints
Ability to work across multiple programming languages and paradigms
A passion for writing robust, intuitive, and pragmatic production code
Ability to navigate and work effectively in large codebases
Strong verbal and written communication skills
Ability to work autonomously and collaborate effectively with colleagues across time zones
Nice to have
Experience with performance evaluation and optimization