Software Engineer (Backend), Enterprise

Навыки

• AI-инструменты в работе
• AWS
• Azure
• CI/CD
• Распределённые системы
• Docker
• FastAPI

О компании и продукте

• At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
• We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
• We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
• We comply with the United States Department of Labor's Pay Transparency provision .

Задачи

• Design, build, and scale backend systems that power enterprise GenAI products, focusing on reliability, performance, and deployment across both Scale’s and customers’ infrastructure
• 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
• Optimize backend performance for latency, throughput, and cost—ensuring AI applications can operate at enterprise scale across hybrid and multi-cloud environments
• Manage and evolve cloud infrastructure (AWS, Azure, or GCP), driving automation, observability, and security for large-scale AI deployments
• Collaborate with ML and product teams to bring cutting-edge GenAI models into production through efficient APIs, model serving systems, and evaluation frameworks
• Continuously improve reliability and scalability , applying strong engineering practices to make AI systems robust, maintainable, and enterprise-ready
• Ideally, You Have
• 4+ years of experience developing large-scale backend or infrastructure systems, with a strong emphasis on distributed services, reliability, and scalability
• Proficiency in Python or TypeScript , with experience designing high-performance APIs and backend architectures using frameworks such as FastAPI, Flask, Express, or NestJS
• Deep familiarity with cloud infrastructure (AWS and Azure preferred), including container orchestration (Kubernetes, Docker) and Infrastructure-as-Code tools like Terraform
• Experience managing data systems such as relational and NoSQL databases (PostgreSQL, DynamoDB, etc.) and building pipelines for data-intensive applications
• Strong understanding of observability, CI/CD , and security best practices for running services in enterprise or multi-tenant environments
• Ability to balance rapid iteration with production-grade quality , shipping reliable backend systems in fast-paced environments
• Collaborative mindset , working closely with ML, infra, and product teams to bring complex GenAI systems into production at enterprise scale
• PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role
• This allows us to ensure a fair and thorough evaluation of all applicants

Требования

• Hands-on experience with GenAI applications, model integration, or AI agent systems—understanding how to deploy, evaluate, and scale AI workloads in production

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