Senior Core Infrastructure Engineer

We’re hiring a Platform Software Engineer to help build the execution layer behind OCI’s AI and GPU growth motion. You’ll work across Oracle data platforms, GPU scheduling, orchestration pipelines, and MLOps agentic tooling — turning customer demand into production-grade systems that scale across our cluster fleet. This is a builder role on a small, high-leverage team.

What you’ll do

• Integrate with Oracle data platforms (OCI Streaming, 23ai, Object Storage, Data Flow) to move training and inference data reliably across customer and internal pipelines.
• Build and extend GPU schedulers and capacity-aware placement logic for A100, H100, H200, and Blackwell fleets.
• Develop orchestration pipelines for training, fine-tuning, and inference workloads using Kubernetes, Argo, and Slurm where appropriate.
• Ship MLOps agentic tooling — observability, automated triage, cost and SLO agents — that reduces operator load on large GPU deployments.
• Partner with PMs, SAs, and customer-facing teams to convert field requirements into reusable components, not one-off scripts

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Disclaimer:

Certain U.S. based or U.S. customer or client-facing roles may be required to comply with applicable requirements, such as immunization/occupational health mandates, and/or drug testing requirements.

Range and benefit information provided in this posting are specific to the stated locations only

US: Hiring Range in USD from: $79,200 to $209,500 per annum. May be eligible for bonus and equity.


Oracle maintains broad salary ranges for its roles in order to account for variations in knowledge, skills, experience, market conditions and locations, as well as reflect Oracle's differing products, industries and lines of business.
Candidates are typically placed into the range based on the preceding factors as well as internal peer equity.

Oracle US offers a comprehensive benefits package which includes the following:
1. Medical, dental, and vision insurance, including expert medical opinion
2. Short term disability and long term disability
3. Life insurance and AD&D
4. Supplemental life insurance (Employee/Spouse/Child)
5. Health care and dependent care Flexible Spending Accounts
6. Pre-tax commuter and parking benefits
7. 401(k) Savings and Investment Plan with company match
8. Paid time off: Flexible Vacation is provided to all eligible employees assigned to a salaried (non-overtime eligible) position. Accrued Vacation is provided to all other employees eligible for vacation benefits. For employees working at least 35 hours per week, the vacation accrual rate is 13 days annually for the first three years of employment and 18 days annually for subsequent years of employment. Vacation accrual is prorated for employees working between 20 and 34 hours per week. Employees working fewer than 20 hours per week are not eligible for vacation.
9. 11 paid holidays
10. Paid sick leave: 72 hours of paid sick leave upon date of hire. Refreshes each calendar year. Unused balance will carry over each year up to a maximum cap of 112 hours.
11. Paid parental leave
12. Adoption assistance
13. Employee Stock Purchase Plan
14. Financial planning and group legal
15. Voluntary benefits including auto, homeowner and pet insurance

The role will generally accept applications for at least three calendar days from the posting date or as long as the job remains posted.

Career Level - IC3

What we’re looking for:

• 3–6 years of software engineering experience, ideally in distributed systems, data platforms, or ML infrastructure.
• Strong Python; working knowledge of Go or Rust is a plus.
• Hands-on experience with Kubernetes, container runtimes, and at least one workflow engine (Argo, Airflow, Prefect, Temporal).
• Familiarity with GPU workloads, CUDA toolchains, or inference serving frameworks (vLLM, TensorRT-LLM, Triton).
• Experience with LLM agent frameworks (LangGraph, CrewAI, or similar) is a strong plus.
• Comfort working in ambiguity, shipping iteratively, and reasoning about topology — not just features.

Nice to have:

• Prior work on multi-cloud or hybrid deployments.
• Exposure to QEC, quantum-classical hybrid workloads, or scientific computing.
• Open-source contributions to the AI infra ecosystem.