> Markdown version of [/jobs/ext/2637685-platform-software-engineer](https://www.wearedevelopers.com/jobs/ext/2637685-platform-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Platform Software Engineer

- **Company:** Oracle
- **Location:** Denver, CO, United States
- **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 1, 2026
- **Apply:** https://dejobs.org/x/x/B053D6C99366479FA4E6A4DCE9C46906/job/

## 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


## Related Videos

- [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated)
- [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops)
- [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms)
- [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment)

## Related Articles

- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers)
- [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)
- [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)