Software Engineer (Enterprise)

Join Scale, a leading company in the AI industry, as a Backend Engineer. In this role, you will build the core infrastructure that powers AI products for some of the world's largest enterprises. You will design scalable APIs, distributed data systems, and robust deployment pipelines, ensuring production-grade reliability and performance. This is a unique opportunity to be at the forefront of the GenAI revolution and make a significant impact on how businesses operate and innovate.

Design, build, and scale backend systems that power enterprise GenAI products, focusing on reliability, performance, and deployment across both Scale’s and customers’ infrastructure.

About Scale AI

If you're excited about pushing the boundaries of AI-driven product development, we want to hear from you

Responsibilities

• Develop core services and APIs that integrate AI models and enterprise data sources securely and efficiently, enabling production-scale AI adoption.
• Architect scalable distributed systems for data processing, inference, and orchestration of large-scale GenAI workloads.

Requirements

• Strong understanding of observability, CI/CD, and security best practices for running services in enterprise or multi-tenant environments
• Hands-on experience with GenAI applications, model integration, or AI agent systems—understanding how to deploy, evaluate, and scale AI workloads in production
• Experience managing data systems such as relational and NoSQL databases (PostgreSQL, DynamoDB, etc.) and building pipelines for data-intensive applications
• Proficiency in Python or TypeScript, with experience designing high-performance APIs and backend architectures using frameworks such as FastAPI, Flask, Express, or NestJS
• Collaborative mindset, working closely with ML, infra, and product teams to bring complex GenAI systems into production at enterprise scale
• 4+ years of experience developing large-scale backend or infrastructure systems, with a strong emphasis on distributed services, reliability, and scalability
• Deep familiarity with cloud infrastructure (AWS and Azure preferred), including container orchestration (Kubernetes, Docker) and Infrastructure-as-Code tools like Terraform
• Ability to balance rapid iteration with production-grade quality, shipping reliable backend systems in fast-paced environments