Senior Software Engineer (Typescript / Frontend) - AI/ML

Senior level: This role suits an experienced software engineer with 5+ years in production environments and strong TypeScript/JavaScript and React expertise.

The candidate should understand AI/ML technologies, API architecture, cloud platforms, database systems, data pipelines, observability, and production model integration, with ClickHouse, vector databases, and data visualizations as advantages.

They should be familiar with building and operating full-stack AI features, developing responsive data-oriented interfaces, integrating inference services and external tools, driving projects from concept to production independently, and participating in on-call support.

How would you design a TypeScript and React frontend for an AI feature in ClickHouse Cloud?

I would separate data-fetching, domain logic, and presentation components, using strongly typed API clients and predictable state management. I would also design for streaming responses, loading and error states, accessibility, responsive layouts, and safe handling of sensitive user data.

How would you implement a streaming interface for AI-generated results?

I would use Server-Sent Events, WebSockets, or a streaming HTTP response, depending on the API contract, and incrementally update the React state as tokens or result chunks arrive. The implementation should support cancellation, reconnection where appropriate, backpressure, partial failures, and clear completion status.

How would you design an API that connects ClickHouse with an external AI inference service?

I would define a versioned, typed contract that normalizes prompts, model parameters, responses, errors, and usage metadata. The service should include authentication, timeouts, retries with backoff, rate limiting, request tracing, validation, and safeguards against leaking sensitive query data.

What considerations are important when integrating AI models into a production system?

Important considerations include model versioning, reproducible configuration, latency and cost limits, evaluation datasets, quality metrics, monitoring, fallback behavior, and rollback procedures. I would also track model and prompt changes so regressions can be identified and audited.

How would you build a React data visualization for large ClickHouse result sets?

I would avoid loading unnecessary rows into the browser by using aggregation, pagination, sampling, or server-side filtering in ClickHouse. On the frontend, I would use virtualized tables or canvas-based charts where needed, memoize expensive transformations, and clearly communicate time ranges, units, and query status.

How would you use ClickHouse for AI or machine learning workloads?

ClickHouse is well suited for high-volume event, feature, telemetry, and evaluation data because of its columnar storage and analytical query performance. I would design schemas around access patterns, use appropriate ordering keys and partitioning, and combine SQL analytics with vector search or external inference services when required.

How would you integrate a vector database or embedding workflow with ClickHouse?

I would define a pipeline that generates embeddings, stores them with stable identifiers and metadata, and keeps source records synchronized through idempotent updates. Depending on requirements, similarity search could use ClickHouse capabilities or a specialized vector store, with attention to freshness, filtering, indexing, and retrieval quality.

How would you secure an AI-powered feature exposed through a web application?

I would enforce authorization on the server rather than relying on frontend controls, validate all inputs, protect credentials, and apply tenant isolation and query-level permissions. I would also redact sensitive data from logs, limit prompt and response sizes, defend against prompt injection where relevant, and use audit logging for model actions.

How would you test an AI feature whose output is not fully deterministic?

I would combine deterministic unit and integration tests for API behavior, permissions, state transitions, and rendering with evaluation tests for response quality. Quality tests should use representative datasets and explicit metrics, while tolerances, snapshots, human review, and regression thresholds help manage model variability.

How would you troubleshoot a production incident involving an AI feature?

I would first assess user impact and stabilize the system through rate limiting, feature flags, fallback models, or rollback. Then I would use logs, metrics, traces, model and prompt versions, and ClickHouse query data to identify the cause, communicate status during on-call, and document preventive actions after recovery.

ClickHouse is a globally distributed data and AI platform company building products for agents, data scientists, and modern analytics teams. As a Senior Software Engineer, you’ll develop AI-powered features, scalable APIs, ML integrations, production monitoring, and intuitive interfaces across ClickHouse Cloud while participating in daytime on-call. The stack includes TypeScript, JavaScript, React, AI/ML inference services, APIs, cloud platforms, vector databases, and ClickHouse. This is a full-time, remote role in Australia offering $147K–$238K plus equity, with healthcare contributions, flexible time off, and a home-office allowance—an excellent opportunity to shape impactful AI products at a fast-growing company.