Senior Software Engineer, Cloud Efficiency & Cost Optimization

Nuro•Mountain View, CA

About The Position

Nuro is a self-driving technology company on a mission to make autonomy accessible to all. Founded in 2016, Nuro is building the world’s most scalable driver, combining cutting-edge AI with automotive-grade hardware. Nuro licenses its core technology, the Nuro Driver™, to support a wide range of applications, from robotaxis and commercial fleets to personally owned vehicles. With technology proven over years of self-driving deployments, Nuro gives the automakers and mobility platforms a clear path to AVs at commercial scale, empowering a safer, richer, and more connected future. This role sits within Nuro’s Core Infrastructure team and partners deeply across every engineering organization — Autonomy, ML Platform, Simulation, Perception, Behavior, Mapping, BATES, and Release Engineering. Cloud is the substrate the entire company runs on. How efficiently we use it directly determines how many miles we can drive, how many models we can train, and how many scenarios we can validate per dollar. This is one of the highest-leverage engineering roles at Nuro: the systems, guardrails, and patterns you build will shape how thousands of jobs, pipelines, and services are designed and operated across the company. The Senior Software Engineer will own cloud efficiency end-to-end, identifying high-ROI opportunities across storage, compute, data, and ML infrastructure, and delivering systems that capture those gains. The role involves driving cost-aware system design, optimizing performance and efficiency at scale for large-scale workloads, building cost observability and attribution systems, eliminating systemic waste through automation, driving cross-organizational optimization, and establishing cost governance processes.

Requirements

Strong software engineering fundamentals in Python or Go, with experience building and operating production systems end-to-end.
Experience with public cloud platforms (GCP preferred) including billing, IAM, and resource management.
Experience with data warehouses (BigQuery, Snowflake, or similar), including query optimization at scale.
Proven track record of delivering measurable improvements in performance, efficiency, or cost on production systems.
Strong system design skills, with the ability to evaluate and influence tradeoffs across cost, performance, reliability, and developer experience.
Ability to operate in ambiguous, cross-team environments and drive initiatives from problem definition to execution.
Excellent communication skills, including presenting tradeoffs to both engineers and executive stakeholders.
Bachelor’s degree in Computer Science, Electrical Engineering, or related field, or equivalent practical experience.

Nice To Haves

Experience in FinOps, cloud cost engineering, or large-scale efficiency platforms.
Deep expertise in GCP at scale (GCS, GKE, BigQuery, Compute Engine, reservations/CUDs, Cloud Monitoring).
Experience with Terraform, Kubernetes, and infrastructure-as-code.
Background in data infrastructure, ML systems, or large-scale batch processing.
Experience building cost attribution, anomaly detection, or forecasting systems on top of cloud billing data.

Responsibilities

Own cloud efficiency end-to-end. Identify the highest-ROI opportunities across storage, compute, data, and ML infrastructure, and deliver systems that capture those gains as durable platform behavior, not one-off cleanups.
Drive cost-aware system design. Partner with teams on architecture and design reviews to ensure new systems are efficient by default. Raise the bar for how engineers reason about storage, retention, data layout, query patterns, compute sizing, and reservation strategy.
Optimize performance and efficiency at scale. Profile and tune large-scale workloads — BigQuery analytics, data pipelines, simulation storage, ML training, and GKE services — turning expensive, slow jobs into efficient, high-throughput systems.
Build cost observability and attribution. Deliver real-time visibility, anomaly detection, and fine-grained attribution (SKU, bucket, workload, team) so issues are identified and resolved in hours, not weeks.
Eliminate systemic waste. Automate lifecycle and tiering (autoclass, archive, scene-based retention), right-size compute, and continuously clean up underutilized or orphaned resources across the fleet.
Drive cross-org optimization. Partner across engineering to land high-impact improvements (e.g., precision reduction, query tuning, data lifecycle strategies) and convert them into guardrails that prevent regression.
Establish cost governance. Define and scale processes for budgeting, forecasting, cost reviews, and incident response as Nuro grows. Enable teams to build cost-efficient systems by default through tooling, standards, and education.