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What You’ll Do

• Own the text-to-SQL accuracy problem end-to-end: design evals, iterate prompts, and improve retrieval/routing
• Build and operate the experimentation and evaluation loop (automatic evals, regression suites, dataset curation)
• Design pragmatic LLM application architectures (RAG, agent routing, tool-use orchestration) optimized for accuracy and latency
• Ship production-grade code and support deployments; instrument, monitor, and troubleshoot model behavior in real customer environments
• Partner closely with engineering and customers to improve semantic models, SQL generation, and data alignment
• Create feedback loops from users to systematically capture issues and convert them into measurable improvements
• Contribute to automation of environment provisioning and dev workflows to enable fast iteration

What We’re Looking For

• 2+ years in ML/AI or data-focused engineering or data science roles building production systems data or AI systems
• Demonstrated experience tuning LLM applications: prompt engineering, evals, retrieval, agent design, or similar
• Strong hands-on coding in Python or TypeScript (TypeScript familiarity a plus; willingness to work across the stack required)
• ML engineering mindset beyond notebooks: testing, CI, observability, performance, and deployment in production
• Comfort with SQL and complex data modeling; familiarity with data warehouses and pipelines
• Pragmatic, product-oriented approach—optimize for impact over novelty; complement existing systems rather than rebuild from scratch
• Ability to design experiments, quantify improvements, and communicate trade-offs clearly

Nice to Have

• Experience with text-to-SQL systems, semantic layers, or BI/analytics workflows
• Exposure to RAG frameworks, knowledge graphs, vector stores, and evaluation tooling
• Prior work in analytics engineering or data engineering environments

Success Looks Like

• Measurable improvements in text-to-SQL accuracy across target datasets and partners
• Reliable eval pipeline and regression suite running in CI to catch degradations
• Clear architecture and documentation for context/agent systems that others can contribute to
• Short feedback cycles with partners leading to fast, meaningful product wins

Compensation

• Competitive salary and meaningful equity
• Comprehensive benefits

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