Oracle Cloud Infrastructure (OCI) is seeking an AI-native Software Developer 3 to join our fast-moving data and platform engineering team. This role is for an engineer with strong general software engineering fundamentals, solid hands-on coding ability, and the curiosity to learn quickly in complex technical domains. Python experience is valuable, but the larger requirement is practical engineering range across the kinds of systems that power operations, planning, and integration at scale. You will work on the kinds of tools and services modern coding agents are good at accelerating: internal platforms, automation, APIs, developer workflows, data-connected services, operational tooling, and shared technical infrastructure. Just as importantly, you will be expected to bring engineering judgment that does not blindly trust generated output.
This is a role for someone who can use AI coding tools as leverage while still owning the architecture, validation, and production quality of what ships.
The Team
We are a technical team that owns tooling and data pipelines for Oracle Cloud’s Capacity and Product Management teams. We work across planning workflows, internal tools, automation, service integrations, and shared infrastructure. Some work looks like data engineering, some looks like platform engineering, and some looks like developer productivity.
Example projects may include optimizing datacenter and server buildout schedules, building source-of-truth systems for the cloud supply chain, developing interfaces that connect critical systems across the ecosystem, and supporting shared networked platforms for hosting AI skills and knowledge centers.
As we grow, we are looking for engineers who can contribute quickly, learn new domains fast, and raise the quality bar for how AI-assisted software gets designed, tested, and maintained.
Internal Responsibilities
Key Responsibilities
Builder: Design and implement production-quality software using the right tools for the problem. Build APIs, services, automations, internal tools, and developer-facing systems that solve concrete business and operational problems.
AI-Native Engineer: Use modern coding agents and AI-assisted development workflows effectively, but with skepticism and discipline. Treat generated code as a starting point, not a finished answer.
Technical Reviewer: Review AI-generated and human-written code for correctness, maintainability, security, observability, and operational fit. Catch weak abstractions, missing edge cases, poor test coverage, and fragile system boundaries.
Systems Thinker: Work across application logic, CI/CD, DevOps workflows, service integration, data-connected systems, and networking fundamentals. Understand how software behaves in real environments, not just in isolated local demos.
Problem Solver: Thrive in ambiguous environments by taking loosely defined problems and turning them into bounded, reviewable engineering steps. Ask good questions, identify the actual source of truth, and avoid cargo-cult implementations.
Continuous Learner: Ramp quickly on new tools, domains, and codebases. Bring curiosity, initiative, and evidence that you build and learn outside the narrow minimum required by a role.
Team Player and Communicator: Collaborate well with peers, product partners, and adjacent technical teams. Communicate tradeoffs, risks, and progress clearly in writing and conversation.
Candidate Profile
2+ years of relevant software engineering experience.
Strong general programming experience across software design, APIs, debugging, testing, and data structures. Python experience is a plus, but broader engineering capability matters more than expertise in one language.
Bachelor's degree in Computer Science, Computer Engineering, or a related technical field.
Experience with developer tooling, CI/CD, DevOps practices, and basic networking concepts.
Comfort working across code, infrastructure, and runtime behavior to diagnose issues end to end.
Experience or strong interest in systems that support planning, operational decision-making, supply chain visibility, systems integration, or shared technical platforms.
Experience using AI coding tools, code generation, or agentic workflows in a practical engineering setting.
Strong judgment about when generated code is acceptable and when it needs to be challenged, rewritten, or rejected.
Evidence of being a passionate learner. This can include side projects, open source work, technical writing, hackathons, clubs, research, teaching, internships, or other sustained extracurricular initiative.
Good written and verbal communication skills.
Ability to operate with partial information and still make disciplined engineering progress.
Preferred Qualifications
Experience building internal tools, workflow automation, or developer productivity systems.
Experience with cloud services, service-to-service integrations, and operational telemetry.
Experience writing reliable tests at multiple layers, including unit, integration, and failure-path coverage.
Experience working in environments where requirements evolve quickly and judgment matters as much as speed.
Experience with scheduling, planning, source-of-truth system design, or cross-system workflow integration is a plus.
External Responsibilities
Key Responsibilities
Builder: Design and implement production-quality software using the right tools for the problem. Build APIs, services, automations, internal tools, and developer-facing systems that solve concrete business and operational problems.
AI-Native Engineer: Use modern coding agents and AI-assisted development workflows effectively, but with skepticism and discipline. Treat generated code as a starting point, not a finished answer.
Technical Reviewer: Review AI-generated and human-written code for correctness, maintainability, security, observability, and operational fit. Catch weak abstractions, missing edge cases, poor test coverage, and fragile system boundaries.
Systems Thinker: Work across application logic, CI/CD, DevOps workflows, service integration, data-connected systems, and networking fundamentals. Understand how software behaves in real environments, not just in isolated local demos.
Problem Solver: Thrive in ambiguous environments by taking loosely defined problems and turning them into bounded, reviewable engineering steps. Ask good questions, identify the actual source of truth, and avoid cargo-cult implementations.
Continuous Learner: Ramp quickly on new tools, domains, and codebases. Bring curiosity, initiative, and evidence that you build and learn outside the narrow minimum required by a role.
Team Player and Communicator: Collaborate well with peers, product partners, and adjacent technical teams. Communicate tradeoffs, risks, and progress clearly in writing and conversation.
Candidate Profile
2+ years of relevant software engineering experience.
Strong general programming experience across software design, APIs, debugging, testing, and data structures. Python experience is a plus, but broader engineering capability matters more than expertise in one language.
Bachelor's degree in Computer Science, Computer Engineering, or a related technical field.
Experience with developer tooling, CI/CD, DevOps practices, and basic networking concepts.
Comfort working across code, infrastructure, and runtime behavior to diagnose issues end to end.
Experience or strong interest in systems that support planning, operational decision-making, supply chain visibility, systems integration, or shared technical platforms.
Experience using AI coding tools, code generation, or agentic workflows in a practical engineering setting.
Strong judgment about when generated code is acceptable and when it needs to be challenged, rewritten, or rejected.
Evidence of being a passionate learner. This can include side projects, open source work, technical writing, hackathons, clubs, research, teaching, internships, or other sustained extracurricular initiative.
Good written and verbal communication skills.
Ability to operate with partial information and still make disciplined engineering progress.
Preferred Qualifications
Experience building internal tools, workflow automation, or developer productivity systems.
Experience with cloud services, service-to-service integrations, and operational telemetry.
Experience writing reliable tests at multiple layers, including unit, integration, and failure-path coverage.
Experience working in environments where requirements evolve quickly and judgment matters as much as speed.
Experience with scheduling, planning, source-of-truth system design, or cross-system workflow integration is a plus.
