Lead Principal Platform Software Engineer

Oracle•Nashville, TN

8d•$113,600 - $306,400•Onsite

About The Position

Oracle’s AI and Robotics team is building software platforms that bring together scientific workflows, AI, robotics, and laboratory automation in support of the Ellison Institute of Technology. Within Oracle Cloud Infrastructure, the team develops shared cloud services, integration frameworks, execution infrastructure, and developer tools for AI-enabled life sciences laboratories. These capabilities enable scientists and application teams to develop and operate automated workflows across robots, scientific instruments, and laboratory equipment. The Lead Principal Software Engineer sets long-term technical direction for the Robotics Automation Platform and leads architecture and technical delivery across services and teams. The role translates emerging customer requirements into reusable platform capabilities. It is a hands-on individual contributor position combining architectural leadership with substantial implementation: guiding principal and senior engineers, building foundational capabilities, and resolving complex issues involving distributed services, AI infrastructure, and equipment integration. Based in Nashville, Tennessee, the engineer collaborates with teams in the United Kingdom and India Development Centre. UK colleagues support physical equipment integration and laboratory validation, working with the role on platform interfaces, simulation, diagnostics, and system verification. The position is fully on-site at Oracle’s Nashville office, with collaboration across geographies through remote technical design, development, integration, and operational activities. The role supports production services through an on-call rotation providing 24×7 incident response coverage.

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.

Nice To Haves

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 is valuable but not essential.

Responsibilities

Own the technical architecture and evolution of shared platform capabilities across cloud services, execution infrastructure, equipment integration, and developer tooling.
Define platform architecture and service boundaries for laboratory workflow orchestration, resource management, robotic fleet coordination, device integration, and local execution.
Lead technical delivery of foundational cloud services, APIs, SDKs, integration frameworks, and developer tools.
Define modular architectures, versioned APIs, typed interfaces, compatibility expectations, and reference implementations.
Establish reusable equipment capability models, adapter contracts, and secure interfaces between cloud services and laboratory systems.
Architect shared execution infrastructure for state management, asynchronous operations, persistence, retries, idempotency, and recovery from partial failures.
Establish patterns for event-driven communication, equipment state representation, command execution, and recovery without unintended repetition of physical actions.
Define infrastructure for agentic capabilities, including tool execution, context management, orchestration, evaluation, and human oversight.
Establish boundaries between AI-driven decisions, deterministic execution, equipment control, and independent physical safety systems.
Personally design, implement, test, and deploy critical platform components and reference implementations; build critical services and resolve difficult implementation, integration, and performance problems.
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; resolve architectural dependencies and guide delivery across those teams.
Enable 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 for availability, latency, resilience, capacity, and recovery.
Establish identity, access control, policy enforcement, secure communications, auditability, and data protection patterns.
Support production services through an on-call rotation providing 24×7 response coverage for service incidents.
Define observability standards for 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.
Mentor principal and senior engineers and establish consistent engineering practices across the platform.

Benefits

Medical insurance
Dental insurance
Vision insurance
Short-term disability
Long-term disability
Life insurance and AD&D
Supplemental life insurance for employees and eligible family members
Health care and dependent care flexible spending accounts
Pre-tax commuter and parking benefits
401(k) savings and investment plan with company match
Flexible vacation for eligible salaried employees or accrued vacation for other eligible employees
11 paid holidays
Paid sick leave
Paid parental leave
Adoption assistance
Employee Stock Purchase Plan
Financial planning and group legal benefits
Voluntary auto, homeowner, and pet insurance benefits
Potential eligibility for bonus, equity, and compensation deferral