Senior Software Engineer (backend) - Ai/ml

ClickHouse ClickHouse · Data AI · EMEA · Engineering

Senior Software Engineer (Backend) role focused on building and operating AI/ML products end-to-end at ClickHouse. Responsibilities include feature development, API architecture for inference systems, ecosystem integrations, production model deployment, and developer experience tools.

What you'd actually do

  1. Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform.
  2. Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external/internal AI services.
  3. Implement and maintain integrations with the broader AI/ML ecosystem and standards, ensuring that ClickHouse as a technology works seamlessly with popular frameworks and tools.
  4. Integrate models into production systems with proper monitoring, versioning, observability, and evaluation.
  5. Design and implement developer tools, SDKs, and documentation that enable users to leverage ClickHouse’s AI/ML capabilities.

Skills

Required

  • 5+ years of software engineering experience in production environments
  • Exposure to working directly with AI/ML technologies
  • Backend development experience in TypeScript or Python
  • API design
  • service architecture
  • high level of ownership
  • drive features from concept to production with minimal supervision
  • collaborative environments
  • communicate technical concepts to diverse stakeholders
  • Backend development experience in Python, Go, or TypeScript

Nice to have

  • Experience integrating and deploying AI/ML models in production systems
  • working with inference APIs
  • vector databases
  • cloud technologies such as AWS, Azure, or GCP
  • services related to AI/ML deployment
  • database systems
  • data processing pipelines
  • ClickHouse experience

What the JD emphasized

  • production environments
  • AI/ML technologies
  • API design and service architecture
  • ownership
  • integrate and deploy AI/ML models in production systems

Other signals

  • AI/ML products end-to-end
  • backend inference services
  • API architecture for AI/ML inference systems
  • integrations with AI/ML ecosystem
  • integrate models into production systems