Machine Learning Engineer (AI/ML)

ClickHouse is a cloud company focused on real-time analytics, data warehousing, observability, and AI workloads. The Machine Learning Engineer will design, build, and operate AI-powered products and features, develop scalable APIs and ecosystem integrations, integrate and evaluate models in production, and own machine learning pipelines end to end.

Responsibilities:

• Feature Development: Design and implement AI-powered features across the full stack, from backend inference services to intuitive frontend interfaces within the ClickHouse Cloud platform
• API Architecture: Create robust, scalable APIs that connect ClickHouse's database capabilities with modern AI/ML inference systems and external/internal AI services
• Ecosystem Integrations: 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
• Technical Integration: Integrate models into production systems with proper monitoring, versioning, observability, and evaluation
• Oncall: Participate in the daytime oncall rotation along with the rest of the team
• ML Engineering: Own the ML pipeline end to end (data, training, evaluation), and fine-tune or train custom models when off-the-shelf ones fall short. Track the state of the art closely enough to apply what genuinely improves our products

Requirements:

• 5+ years of software engineering experience in production environments
• Exposure to working directly with AI/ML technologies
• Experience integrating and deploying AI/ML models in production systems, including working with inference APIs and vector databases
• A high level of ownership and can drive features from concept to production with minimal supervision
• Ability to thrive in collaborative environments and can effectively communicate technical concepts to diverse stakeholders
• Applied ML experience in Python (Jupyter, PyTorch, common ML libraries), hands-on with compute clusters (Ray, Slurm), and designs trustworthy experiments to drive architectural decisions
• Familiarity with cloud technologies such as AWS, Azure, or GCP, particularly services related to AI/ML deployment
• Understanding of database systems and data processing pipelines, with ClickHouse experience being a significant plus
• Experience with embedding-based systems (retrieval, ranking, recommenders) and the representation-learning ideas underneath them