O

Oracle

Builds databases, business apps and cloud services

Lead Principal Platform Software Engineer

Backend EngineeringConfirmed live in the last 24 hours
Full-timeNashville, TN, USA

About the Job

Requirements

  • At least 8 years of professional software engineering experience, including sustained technical leadership for complex production platforms or distributed systems.
  • Demonstrated architectural ownership across multiple services and teams, from ambiguous requirements through deployment and ongoing operation.
  • Deep expertise in cloud-native distributed systems, service design, APIs, and integration frameworks.
  • Strong, current programming skills in Python, Go, Java, C++, or similar languages, with evidence of personally delivering substantial production software.
  • Experience building infrastructure for agentic systems, including tool integration, context and state management, orchestration, evaluation, human review, and failure recovery.
  • Strong understanding of concurrency, asynchronous execution, message delivery, persistence, consistency, and recovery across distributed components.
  • Experience defining secure interfaces, data models, service contracts, and engineering standards that allow teams to deliver compatible capabilities independently.
  • Ability to resolve complex technical trade-offs across reliability, security, scalability, performance, cost, and delivery priorities.
  • Proven ability to influence cross-team decisions, mentor experienced engineers, and communicate technical strategy to technical and non-technical stakeholders.
  • Experience collaborating across geographically distributed teams, with clear technical documentation and effective asynchronous communication.
  • Bachelor's degree in computer science, Robotics, Engineering, or a related discipline, or equivalent practical experience.
  • Ability to support production services through an on-call rotation providing 24×7 response coverage for service incidents.

Responsibilities

  • Own the technical architecture and evolution of shared platform capabilities across cloud services, execution infrastructure, equipment integration, and developer tooling.
  • Define modular architectures, versioned APIs, typed interfaces, compatibility expectations, and reference implementations.
  • Build critical services and resolve difficult implementation, integration, and performance problems.
  • Establish patterns for event-driven communication, equipment state representation, command execution, and recovery without unintended repetition of physical actions.
  • Define scientific data-ingestion and metadata interfaces that preserve experimental context, provenance, and traceability.
  • Lead cross-team design and code reviews, document decisions, and ensure clean, maintainable, secure, and well-tested implementations.
  • Establish AI-assisted development practices supported by rigorous review, testing, and validation.
  • Partner with product managers, scientists, customers, and engineering leaders to understand workflows, constraints, and strategic requirements.
  • Translate customer scenarios into platform capabilities, technical roadmaps, acceptance criteria, and achievable delivery increments.
  • Validate architectural assumptions through prototypes, customer feedback, and operational evidence.
  • Guide the transition from pilot integrations to reusable capabilities supporting multiple laboratories, vendors, applications, and customers.
  • Coordinate technical dependencies and integration milestones across UK, India Development Centre, and US teams, enabling rapid iteration while maintaining engineering quality and clear ownership across service boundaries.
  • Define verification strategies spanning unit, integration, functional, performance, resilience, and system-level testing.
  • Establish simulation, equipment emulation, and hardware-in-the-loop integration frameworks with UK-based engineers responsible for physical laboratory testing.
  • Define acceptance criteria for equipment interfaces, execution semantics, fault recovery, and compatibility.
  • Establish evaluation infrastructure for AI-enabled capabilities, including tool correctness, context handling, execution validation, and human approval.
  • Ensure platform designs respect equipment operating limits, interlocks, and safety boundaries defined by controls and safety specialists.
  • Lead investigations into failures spanning cloud services, communications, local execution, and equipment interfaces.
  • Define service objectives and operational readiness expectations across availability, latency, resilience, capacity, and recovery.
  • Establish identity, access control, policy enforcement, secure communications, auditability, and data protection patterns.
  • Define observability standards covering monitoring, logging, metrics, telemetry, health checks, and alerts.
  • Lead complex incident investigations, root-cause analysis, and corrective actions across service and team boundaries.
  • Follow OCI standards for secure development, reliability, availability, privacy, and operational readiness.
  • Establish deployment automation, runbooks, support documentation, compatibility management, and recovery procedures.

Desired Qualifications

  • Experience with OCI, AWS, Azure, Google Cloud, or another cloud platform.
  • Deep experience with Kubernetes, Docker, microservices, distributed data systems, event-driven architectures, or workflow orchestration.
  • Experience designing cloud-to-edge platforms, remote equipment management, or distributed orchestration of physical systems.
  • Experience with robotics or industrial integration technologies such as ROS 2, DDS, MQTT, OPC UA, or vendor equipment SDKs.
  • Experience with laboratory software platforms, electronic laboratory notebooks (ELN), laboratory information management systems (LIMS), SiLA 2, or instrument integration frameworks.
  • Experience building simulation, hardware-in-the-loop testing, or evaluation infrastructure for robotics or AI-enabled systems.
  • Experience with workflow engines, scheduling, planning, or shared-resource coordination.
  • Experience with data engineering, scientific data pipelines, metadata management, data provenance, or data governance.
  • Experience with AI/ML platforms, model training, model serving, retrieval-augmented generation (RAG), evaluation harnesses, AI agents, Model Context Protocol (MCP), or enterprise AI integrations.
  • Experience supporting scientific, research, life-sciences, or enterprise platform workloads.
  • Experience evolving customer pilots into extensible platform offerings and driving adoption across multiple teams or customers.
  • Previous laboratory automation experience.

About the Company

Oracle

Builds databases, business apps and cloud services

Oracle builds databases, business applications, and cloud infrastructure for companies and public-sector organizations. Oracle Database stores and processes business data, while Oracle Cloud Infrastructure provides computing, storage, and networking for applications and AI workloads. Fusion Cloud Applications support finance, human resources, supply chains, and customer service, helping businesses manage everyday operations and reporting. Customers pay for cloud subscriptions and usage, software licenses, support, hardware, and consulting, with deployment options in Oracle's cloud or their own data centers. Oracle was founded in 1977 and is headquartered in Austin, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Austin, TX
Founded
1977

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