HartleyCo
Member of Technical Staff - AI Data Engineer
San Francisco (In-Office)
$150K to $225K + Equity
A high-growth, AI-native startup coming out of stealth is hiring AI Data Engineers to build the systems that power production-grade AI. The company has recently signed a Series A term sheet and is scaling rapidly. This role is central to unblocking current bottlenecks across data engineering, context modeling, and agent performance.
Responsibilities:
Build distributed, reliable data pipelines using Airflow, Temporal, and n8n Model SQL, vector, and NoSQL databases (Postgres, Qdrant, etc.) Build API and function-based services in Python Develop custom automations (Playwright, Stagehand, Zapier) Work with AI researchers to define and expose context as services Identify gaps in data quality and drive changes to upstream processes Ship fast, iterate, and own outcomes end-to-end
Required Experience:
Strong background in data engineering Hands-on experience working with LLMs or LLM-powered applications Data modeling skills across SQL and vector databases Experience building distributed systems Experience with Airflow, Temporal, n8n, or similar workflow engines Python experience (API/services) Startup mindset and bias toward rapid execution
Nice To Have:
Experience with stream processing (Flink) dbt or Clickhouse experience CDC pipelines Experience with context construction, RAG, or agent workflows Analytical tooling (Posthog)
What You Can Expect:
High-intensity, in-office environment Fast decision-making and rapid shipping cycles Real ownership over architecture and outcomes Opportunity to work on AI systems operating at meaningful scale Competitive compensation package Meals provided plus full medical, dental, and vision benefits
If this sounds like you, please apply now.
A high-growth, AI-native startup coming out of stealth is hiring AI Data Engineers to build the systems that power production-grade AI. The company has recently signed a Series A term sheet and is scaling rapidly. This role is central to unblocking current bottlenecks across data engineering, context modeling, and agent performance.
Responsibilities:
Build distributed, reliable data pipelines using Airflow, Temporal, and n8n Model SQL, vector, and NoSQL databases (Postgres, Qdrant, etc.) Build API and function-based services in Python Develop custom automations (Playwright, Stagehand, Zapier) Work with AI researchers to define and expose context as services Identify gaps in data quality and drive changes to upstream processes Ship fast, iterate, and own outcomes end-to-end
Required Experience:
Strong background in data engineering Hands-on experience working with LLMs or LLM-powered applications Data modeling skills across SQL and vector databases Experience building distributed systems Experience with Airflow, Temporal, n8n, or similar workflow engines Python experience (API/services) Startup mindset and bias toward rapid execution
Nice To Have:
Experience with stream processing (Flink) dbt or Clickhouse experience CDC pipelines Experience with context construction, RAG, or agent workflows Analytical tooling (Posthog)
What You Can Expect:
High-intensity, in-office environment Fast decision-making and rapid shipping cycles Real ownership over architecture and outcomes Opportunity to work on AI systems operating at meaningful scale Competitive compensation package Meals provided plus full medical, dental, and vision benefits
If this sounds like you, please apply now.