Software Engineer, Nuro

• Experience: 3+ years of industry software engineering experience
• Software Engineering: Strong proficiency in Python and working proficiency in C++. You write clean, testable, well-documented code and care about long-term maintainability
• Data Engineering: Experience building data pipelines, ingestion, transformation, storage, and visualization. Familiarity with SQL and analytical workflows
• Systems & Infrastructure: Deep comfort with Linux systems — you've configured kernels, debugged boot issues, written systemd units, or managed bare-metal infrastructure. You understand networking, storage, and compute at a level beyond "it just works."
• Technical Leadership: Experience setting technical vision and roadmap for a project or platform, driving alignment across multiple stakeholders. You've independently identified the cross-functional partners needed to unblock and deliver, and you've briefed senior engineering leadership on trade-offs and recommendations
• AI-Native: You treat AI as a core part of your engineering workflow, not an occasional shortcut — you use agentic tooling (e.g., Claude Code) across the development lifecycle and you understand the boundaries of when AI output demands extra scrutiny versus when it accelerates you
• Bias for Action: Comfortable operating in ambiguous, fast-moving environments where you need to balance long-term architecture with short-term delivery

Desirable

• Engineers across the company rely on this platform daily to answer questions like:
• How will my new ML model affect contention on the GPU?
• How does a new data format impact onboard logging rate or network contention as more data might be flowing from through the system?
• How much memory should be allocated for this new module, and how does it fit into the overall system budget?
• You'll be responsible for development, integration, and the evolution of this platform — from the bare-metal OS and networking layer through the job orchestration and CI/CD integration up to the data analysis and visualization layer
• This is a high-ownership, high-autonomy role on a small team where your work directly gates the release velocity of the entire autonomy stack
• You'll be the technical DRI for the platform — setting the roadmap, making architectural calls, representing the platform's needs to the leadership team, and ensuring the system scales through multiple hardware generations
• Benchmarking Infrastructure: You'll develop and maintain the job orchestration layer that schedules, executes, and validates autonomy performance benchmarks across a fleet of physical bench-top systems — integrated into CI/CD pipelines as merge-blocking and release-blocking quality gates
• Platform Reliability & Observability: Build monitoring, alerting, and self-healing automation for the bench fleet
• Proactively identify systemic risks — capacity bottlenecks, hardware degradation patterns, infrastructure single points of failure — before they become outages
• Track utilization, failure rates, and capacity trends to ensure the platform scales ahead of organizational demand
• Performance Data Pipelines: Design and build end-to-end data pipelines that capture fine-grained performance metrics (CPU/GPU utilization, memory bandwidth, E2E latency, scheduling jitter) from bench-top runs, process them at scale, and surface actionable insights through dashboards and automated regression detection
• Statistical Analysis & Experimentation: Work with Data Science to develop rigorous experimentation methodology for performance results from non-deterministic autonomy workloads — including variance analysis, significance testing, and regression detection
• Bare-Metal & OS Platform: Guide the SRE team through the OS and system-level configuration of bench hardware — including Linux kernel tuning, boot infrastructure, networking, and hardware bring-up — ensuring the platform faithfully reproduces production robot compute behavior
• Drive Platform & Allocation Strategy: Own the planning lifecycle for the benchmarking fleet across hardware generations
• Partner with engineering and program leadership to negotiate hardware allocation, model utilization scenarios under real-world constraints, and present data-backed trade-off recommendations — balancing testing coverage, user throughput, cost, and SLA commitments against finite physical resources
• Cross-Functional Collaboration: Partner with Hardware Engineering, NPI (New Product Introduction), SRE (Site Reliability Engineering), Perception, Behavior, and Data Science teams to translate their performance analysis needs into robust, self-service infrastructure