Senior Director, Platform Software Engineering

Oracle•Seattle, WA

8h•$141,200 - $414,400

About The Position

Oracle Cloud Infrastructure (OCI) is developing scientific platform capabilities that connect computational workflows, scientific data, artificial intelligence, and physical experimentation. The Senior Director of Engineering will establish and scale the engineering organization, deliver initial customer commitments, and develop reusable services for additional scientific programs and customers. This technically demanding people leadership role is accountable for engineering strategy, delivery, quality, security, operations, and economics. The role leads multiple teams and develops engineering managers and senior technical leaders. In partnership with Product Management, the System Architect, and scientific leadership, the Senior Director translates evolving requirements into clear service boundaries, credible delivery plans, and dependable customer outcomes. The role will build an AI-native engineering organization in which agents perform substantial engineering work through autonomous execution, automated validation, and operational feedback. Teams will own the full lifecycle of their services and make technical decisions within agreed architectural boundaries. The Senior Director strengthens team capability and accountability while resolving material technical and organizational issues.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
Substantial experience building and operating complex software or cloud services, including 8+ years of engineering management or technical leadership and 2+ years leading an organization that includes other managers or technical leads; these periods may overlap.
Demonstrated success leading multiple engineering teams, recruiting senior technical talent, developing managers and technical leaders, and delivering complex initiatives across organizational boundaries.
Strong technical judgment in distributed systems, cloud services, data platforms, or comparable systems, including the ability to challenge designs, evaluate tradeoffs, diagnose technical risks, and lead consequential engineering decisions.
Experience owning production delivery and operations, including reliability, security, incident response, and cost, with a record of meeting customer commitments.
Experience applying AI agents and automated validation to engineering workflows, with evidence of improvements in delivery, quality, or operational effectiveness.
Ability to create clarity in evolving environments, lead organizational change, and communicate technical decisions, resource needs, and risks effectively with customers and senior leaders.

Nice To Haves

Experience developing robotics, autonomous systems, or industrial automation products, including integrating software with hardware, sensors, or control systems.
Experience building scientific computing, genomics, bioinformatics, pathogen research, research data, or reproducible workflow capabilities.
Experience developing laboratory automation or scientific instrumentation systems, including equipment integration, state management, testing, and recovery.
Experience scaling emerging cloud services from strategic customer pilots through production operation and repeatable customer adoption.
Experience developing secure agent execution environments, engineering evaluation systems, automated testing and deployment, or feedback loops that reduce manual intervention while maintaining quality.
Background in robotics, automation, scientific computing, or other complex technology fields.

Responsibilities

Own engineering strategy and outcomes across scientific computing, data, AI, and laboratory automation within agreed product direction and system architecture; translate customer priorities into delivery commitments, capacity plans, and investment recommendations.
Build autonomous teams with one accountable owner for each service, establishing ownership of design, implementation, testing, deployment, operations, support, reliability, and cost; delegate technical decisions according to capability and risk.
Lead AI-native engineering transformation across development, testing, deployment, and operations; establish agent execution loops, evaluation harnesses, automated validation, and appropriate human oversight.
Partner with the System Architect and senior engineers on service boundaries, interfaces, interoperability, and system constraints; exercise judgment on consequential decisions while enabling teams to own service architecture and implementation.
Recruit, develop, and evaluate engineering managers, senior engineers, and technical leaders; maintain technical standards, coach future leaders, and design effective management spans and organizational structures.
Own delivery from initial customer pilots through production operation and repeatable deployment; resolve technical risks, integration dependencies, and resource constraints, and communicate changes to commitments early with options and recommendations.
Work with customer scientists, applied science, product managers, and engineering partners to translate research workflows into requirements and acceptance plans; distinguish implementation correctness from scientific validity and establish suitable evaluation with domain experts.
Lead engineering integration across cloud services, workflow engines, scientific applications, laboratory systems, and equipment partners; establish reusable interfaces, configuration, validation, and onboarding approaches while preserving local equipment control and safety authority.
Establish operational readiness, service objectives, observability, incident response, recovery, and continuous improvement; hold service owners accountable for secure operation, customer support, and sustained service performance.
Own engineering capacity, budgets, infrastructure consumption, and service cost; make staffing and technical investment recommendations based on delivery needs, operational obligations, measured productivity, and product economics.
Measure throughput, delivery predictability, quality, reliability, engineering cost, and human intervention in agent workflows; use evidence to improve engineering practices, team composition, and deployment efficiency.
Lead across geographically distributed teams, customer environments, and partner organizations; clarify ownership, manage work transitions, resolve disagreements, and represent engineering decisions and risks to senior executives and customers.

Benefits

Medical, dental, and vision insurance, including expert medical opinion
Short-term and long-term disability
Life insurance and AD&D
Supplemental life insurance for employees, spouses, and children
Health care and dependent care flexible spending accounts
Pre-tax commuter and parking benefits
401(k) Savings and Investment Plan with company match
Flexible or accrued paid vacation, depending on employee eligibility and position
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
Potential eligibility for bonus, equity, and compensation deferral