Senior R&D Software Engineer, Fivetran AI
$163,921–$196,713 year
HybridAustin, Texas, United States
Job Summary
Research emerging AI retrieval and reasoning techniques, then prototype and ship production-grade features for the Fivetran AI platform. Build and maintain both back-end and front-end systems, including Agents Schema pipelines and the Context Catalog UI, while developing AISQL capabilities that ground natural language queries in dbt metric definitions. Take ownership of production reliability through on-call rotation, incident response, and SRE work, and use coding agents to automate repetitive tasks. Partner with product to shape the roadmap and contribute to hiring by participating in the interview process.
Required Qualifications
- 5+ years of programming experience across Python and/or Java
- ability to move fluidly between back-end and front-end work
- Considered a trusted expert in your subject area
- capable of executing complex, ambiguous tasks with minimal hand-holding
- Comfortable reading AI/ML research and turning promising findings into working prototypes
- Strong product and market awareness
- ability to judge which emerging techniques are worth building versus which are hype
- Experience with SQL and data warehouses (BigQuery, Snowflake, Databricks, or similar)
- Genuine willingness to work across the full stack: research, backend, frontend, SRE, and QA
- Writes well-structured, performant code
- can dive into unfamiliar codebases to suggest improvements
- Experience using coding agents or similar AI tooling to speed up day-to-day engineering work
- Analytical mindset to identify gaps in existing systems and design practical, pragmatic improvements
- Thrives in a startup-like environment with shifting priorities and a high degree of ownership
- This is a full-time hybrid position based out of our Austin, TX office
- Our hybrid work model offers a blend of remote flexibility and in-person collaboration, including two days in the office each week to connect and build as a team
Desired Qualifications
- Experience with LLM-powered applications — RAG pipelines, embeddings, evaluation frameworks, or agent frameworks
- Familiarity with semantic layers — dbt, LookML, Sigma, or similar
- Experience with the MCP (Model Context Protocol) ecosystem or building AI agent integrations
- Background in site reliability engineering — monitoring, alerting, incident response
- Experience in data processing (ETL, ELT) and/or building data connectors
- Experienced working in a cloud environment utilizing AWS, GCP, Kubernetes, or Docker
- Contributions to open source AI or data projects
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