Backend Engineer

Summary

Join Calendly's Engineering team as a Backend Engineer to design and build our next-generation AI Platform. You will develop highly scalable, reliable, and performant backend systems for AI services. Collaborate with ML engineers, product managers, and other partners to architect tools and services that accelerate AI capabilities. A typical day involves architecting, implementing, and maintaining backend services and APIs for AI-driven products. You will build scalable infrastructure for machine learning models, integrate third-party AI/ML solutions, and implement security best practices. You will also collaborate with ML engineers and data scientists, develop testing frameworks, and participate in technical design reviews. This role offers a unique opportunity to shape foundational services for next-generation AI solutions.

Requirements

• Professional experience (typically 5+ years) as a backend engineer or in a similar software engineering role, ideally supporting data or ML systems
• Hands-on experience building distributed systems, GraphQL and RESTful APIs, and/or microservices architectures
• Strong proficiency in at least one backend programming language (such as Python, Go, Java, or Scala)
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes)
• Familiarity with storage solutions relevant for AI (e.g., PostgreSQL, MongoDB, Redis, object storage, data lakes)
• Knowledge of security, privacy, and compliance principles in distributed/cloud environments
• Comfortable with CI/CD pipelines, version control systems (Git), and infrastructure-as-code

Responsibilities

• Architect, implement, and maintain backend services and APIs for AI-driven products and features
• Build scalable infrastructure to support large-scale training, deployment, and monitoring of machine learning models
• Integrate third-party AI/ML solutions where appropriate and optimize system performance
• Implement security, compliance, and data privacy best practices for AI workflows
• Collaborate cross-functionally with ML engineers and data scientists to enable seamless model deployment and management
• Develop testing frameworks and monitoring tools to ensure system reliability and observability
• Participate in technical design reviews and provide input on best practices and emerging technologies
• Debug production issues across services and multiple levels of the stack

Benefits

• Quarterly Corporate Bonus program (or Sales incentive)
• Equity awards
• Competitive benefits