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# Platform Software Engineer

- **Company:** Oracle
- **Location:** Redwood City, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $92,500.0 - $209,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Nvidia CUDA, Distributed Systems, Python (Programming Language), Oracle Warehouse Builder, SAS (Software), Scientific Computating, Software Engineering, Data Streaming, System Software, Large Language Models, Multi-Cloud, Kubernetes, Free and Open-Source Software, Data Management, Slurm, Machine Learning Operations, TensorRT, Oracle Cloud Infrastructure
- **Published:** August 5, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/p9zp0rsquk

## About the Role

* 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.

## Description

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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