Fullstack AI Platform Engineer (Python, Rust, Kubernetes)

TL;DR

Fullstack AI Platform Engineer (Python, Rust, Kubernetes): Build and evolve an AI platform that unifies compute, environments, evaluations, secure sandboxes, training, and deployment with an accent on backend feature development, distributed training infrastructure, and high-performance observability. Focus on designing REST APIs and real-time monitoring, implementing Rust-based systems for distributed training and networking, and automating cloud/container operations for heterogeneous GPU/CPU/TPU scheduling.

Location: San Francisco

Company

hirify.global builds an open superintelligence stack and an AI platform for frontier-scale post-training and deployment.

What you will do

• Build web interfaces for AI workload management, monitoring, and resource/job control.
• Develop REST APIs and backend services in Python to support new platform capabilities.
• Create real-time monitoring and debugging tools for platform operations.
• Design and implement distributed training infrastructure in Rust, including high-performance networking and coordination components.
• Build infrastructure automation pipelines with Ansible and manage cloud resources and container orchestration.
• Implement scheduling systems for heterogeneous hardware (CPU, GPU, TPU) and integrate new features into existing infrastructure with reliability and security.

Requirements

• Strong Python backend development (FastAPI, async) and RESTful API design/implementation.
• Modern frontend development experience (TypeScript, React/Next.js, Tailwind) and building developer tools/dashboards.
• Systems programming experience with Rust.
• Infrastructure automation experience (Ansible, Terraform) and container orchestration with Kubernetes.
• Observability experience with Prometheus and Grafana.
• Experience with GPU computing and ML infrastructure, including knowledge of AI/ML model architecture and training.

Culture & Benefits

• Hybrid role combining AI platform software engineering and infrastructure work.
• Open development culture with encouragement to contribute to the broader AI community via research and open-source.
• Focus on potential over perfection.
• Work at the intersection of frontier research, real infrastructure, and go-to-market for an emerging category.