Platform ML Infra Engineer: GPU, Kubernetes & MLOps

Oracle; United States; On-site; USD 92,500 - 209,500; Full time

Benefits offered by this job

Health insurance

401(k) match

Paid time off

Equity

Disability insurance

Job summary

Oracle is seeking a Platform Software Engineer to build the execution layer behind OCI’s AI and GPU growth. You’ll work across Oracle data platforms, GPU scheduling, orchestration pipelines, and MLOps 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. The role emphasizes hands‑on distributed systems work with Kubernetes, Python, and modern ML infra tooling.

Qualifications

• 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.

Responsibilities

• Integrate with Oracle data platforms to move training and inference data across pipelines.
• Build and extend GPU schedulers and capacity-aware placement logic for diverse fleet.
• Develop orchestration pipelines for training, fine-tuning, and inference workloads using Kubernetes, Argo, and Slurm.
• Ship MLOps tooling — observability, automated triage, cost and SLO agents — to reduce operator load.
• Collaborate with PMs, SAs, and customer-facing teams to convert field requirements into reusable components.

Skills

Python

Go

Rust

Kubernetes

Argo

Airflow

Prefect

Temporal

CUDA

LangGraph

CrewAI

Tools

Docker

Slurm

TensorRT-LLM

Job description

Oracle is seeking a Platform Software Engineer to build the execution layer behind OCI’s AI and GPU growth. You’ll work across Oracle data platforms, GPU scheduling, orchestration pipelines, and MLOps 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. The role emphasizes hands‑on distributed systems work with Kubernetes, Python, and modern ML infra tooling.