ClearlyRated

Backend Engineer

• Real systems that enterprise clients depend on. Not greenfield experiments, but a world class brand new platform with production code that has to be correct, observable, and resilient:
• Integration layer that ingests structured data from enterprise sources (Snowflake, BigQuery, SQL databases) and routes it through our platform
• A Snowflake-to-BigQuery bridge over ODBC: custom infrastructure work that will serve as the pattern for all future enterprise integrations
• Webhook-triggered survey automation engine. Business events in the client’s system translate into survey sends through a reliable, auditable event pipeline
• REST APIs consumed by our frontend, enterprise integrations, and third-party clients — you’ll design contracts that have to last
• Event-driven microservices on GCP Pub/Sub and Kafka — async processing, backpressure handling, dead-letter queues, the full picture
• Security and SSO integration for enterprise clients with complex credential and access models
• Permission and access control systems that reflect matrix organizational hierarchies — the authorization layer is genuinely interesting here

Backend Platform Engineer

Frontend Engineer

• Dashboard and data visualization components that surface NPS scores, client sentiment trends, and relationship health data across complex enterprise org structures
• Permission-aware UI that adapts to matrix organizational hierarchies — the same data looks different depending on who you are and where you sit in the business
• Survey automation interfaces connected to real-time event streams — when a webhook fires, something happens in the UI
• Configurable digest and reporting builder for senior leadership, with composable layout and export capabilities
• Component library and design system work that gets used by every engineer on the team
• Performance-critical rendering: we deal with large datasets, deep trees, and users who notice when things are slow

Machine Learning Engineer

• Our AI roadmap is live and shipping. You’ll work on systems that go from training and evaluation to production monitoring:
• Survey timing optimization model — an ML system that learns the optimal moment to send a survey for each client relationship, maximizing response rates and data quality
• NLP pipeline for free-text feedback analysis — classifying, scoring, and extracting structured signals from open-ended survey responses across thousands of enterprise clients
• Client health scoring — an aggregate model that combines survey results, response patterns, historical sentiment, and relationship signals into a single predictive score per account
• Agentic AI architecture using LLM orchestration (Google Vertex AI / ADK) — multi-agent systems that reason over client data and surface proactive recommendations to account managers
• RAG system over enterprise knowledge bases — grounding LLM outputs in verified client data and platform knowledge
• MLOps infrastructure: model versioning, A/B testing, inference cost monitoring, drift detection, and production observability for agent loops

Backend Platform Engineer

Frontend Engineer

AI/ML Engineer