Senior Principal Software Engineer, Core Infrastructure

Oracle; Santa Clara (CA); On-site; USD 190,000 - 235,000; Full time

Job summary

Oracle Cloud Infrastructure (OCI) is seeking a Senior Principal Software Engineer to build a new Data Processing Unit (DPU) platform spanning networking and storage. You will own a major subsystem and shape architectural boundaries, alignment across component owners, and production readiness.

You will lead delivery across teams, set end-to-end performance targets for throughput and tail latency, evaluate data path decisions across host, DPU software, firmware and hardware, and mentor engineers

Qualifications

• Own subsystem architecture and outcomes.
• Shape foundational data path decisions.
• Lead delivery across component owners.
• Set end-to-end performance expectations.
• Connect storage and networking architecture.
• Own integration and production readiness.
• Develop component owners.
• Establish reusable AI practices.

Responsibilities

• Own subsystem architecture and outcomes.
• Shape foundational data path decisions.
• Lead delivery across component owners.
• Set end-to-end performance expectations.
• Connect storage and networking architecture.
• Own integration and production readiness.
• Develop component owners.
• Establish reusable AI practices.

Job description

About the role

Oracle Cloud Infrastructure (OCI) is seeking a Senior Principal Software Engineer to build a new Data Processing Unit (DPU) platform spanning networking and storage. You will address system-level questions such as host-versus-DPU responsibilities, predictable tail latency under mixed workloads, and correct behavior across offload transitions, failures, and upgrades.

You will own a major subsystem spanning components and teams. Depending on the assignment, that could include a storage I/O pipeline, packet processing pipeline, host-to-DPU data path, or infrastructure acceleration subsystem. You will define architectural boundaries, align component owners, and remain accountable for integration and subsystem outcomes. We welcome engineers with deep storage data path expertise and working familiarity with networking, or deep networking data path expertise and working familiarity with storage. Your primary ownership will draw on your strengths while requiring collaboration across both domains.

What you will do

• Own subsystem architecture and outcomes.Turn ambiguous system requirements into an architecture, component contracts, and a phased delivery plan. Establish decision authority and resolve tradeoffs that individual component owners cannot settle independently.
• Shape foundational data path decisions.Evaluate partitioning across host, DPU software, firmware, and hardware. Validate choices about queueing, memory ownership, acceleration, and recovery through prototypes and representative workloads.
• Lead delivery across component owners.Decompose subsystem work, align technical dependencies, and resolve integration risks across teams. Guide implementation through other engineers while personally implementing or debugging critical paths.
• Set end-to-end performance expectations.Establish subsystem budgets and targets for throughput, tail latency, resource efficiency, and relevant storage I/O or packet processing metrics. Resolve bottlenecks that span component boundaries.
• Connect storage and networking architecture.Work with engineers in both domains to evaluate I/O processing, transport, ordering, buffering, backpressure, and recovery tradeoffs. Ensure local optimizations preserve end-to-end performance, availability, and data integrity.
• Own integration and production readiness.Lead subsystem qualification and technical readiness reviews. Establish compatible interfaces, observability, fault isolation, and safe upgrade behavior; resolve systemic risks during rollout and operation.
• Develop component owners.Mentor engineers in architectural judgment and systems debugging. Establish shared engineering practices and help component leaders make sound decisions independently.
• Establish reusable AI practices.Guide effective AI use across subsystem engineering work, with validation and clear accountability. Share approaches that improve quality and productivity, reduce repetitive debugging and documentation, and support employee well-being.