Full Stack Engineer (AI)

TL;DR

Full Stack Engineer (AI): Designing and developing web-based systems for managing AI workloads across supercomputing clusters with an accent on building intuitive interfaces and optimizing resource allocation. Focus on collaborating with researchers and infrastructure teams to deliver scalable solutions that enhance productivity.

Location: Hybrid (3 days in the office per week in San Francisco, CA)

Salary: $255K – $490K + Offers Equity

Company

hirify.global is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity.

What you will do

• Design and develop full-stack web applications to track, monitor, and manage large-scale AI workloads in real time.
• Collaborate with researchers and infrastructure teams to translate complex operational needs into intuitive UIs and scalable backends.
• Build data visualization tools to provide insights into job scheduling and resource allocation.
• Optimize backend services to handle massive data throughput while ensuring low-latency performance and high availability.
• Implement frontend components that provide seamless interactions with scheduling, storage, and compute systems.
• Ensure system security, reliability, and scalability across globally distributed supercomputing infrastructure.

Requirements

• Significant experience in full-stack development, with expertise in modern frontend frameworks (React, Vue, or Angular) and backend technologies (Python, Go, or Node.js).
• Experienced in building scalable, high-performance web applications for complex distributed systems.
• Strong understanding of RESTful and GraphQL APIs, distributed databases, and cloud infrastructure (especially Azure).
• Execution-focused with a keen eye for usability, performance, and scalability in enterprise-scale systems.
• Comfortable working in fast-paced, highly collaborative environments with tight timelines and evolving priorities.

Nice to have

• Experience working with Kubernetes, Docker, and cloud-native application deployment.
• Understanding of AI/ML workload scheduling and orchestration challenges.
• Experience with real-time data processing, visualization libraries, and observability tooling.