AI Engineer

<div class="content-intro">About BobsledBobsled is building AI-powered analytics experiences that turn natural language into accurate, production-grade insights. Our mission is to enable enterprise customers to leverage the full power of AI and data agents, transforming how they access and act on their data. As we scale our AI product, we’re seeking hands-on specialists to ensure our customers’ deployments are robust, contextually tuned, and delivering measurable value.</div>What You’ll Do<ul><li style="font-size: 10pt;">Own the text-to-SQL accuracy problem end-to-end: design evals, iterate prompts, and improve retrieval/routing</li><li style="font-size: 10pt;">Build and operate the experimentation and evaluation loop (automatic evals, regression suites, dataset curation)</li><li style="font-size: 10pt;">Design pragmatic LLM application architectures (RAG, agent routing, tool-use orchestration) optimized for accuracy and latency</li><li style="font-size: 10pt;">Ship production-grade code and support deployments; instrument, monitor, and troubleshoot model behavior in real customer environments</li><li style="font-size: 10pt;">Partner closely with engineering and customers to improve semantic models, SQL generation, and data alignment</li><li style="font-size: 10pt;">Create feedback loops from users to systematically capture issues and convert them into measurable improvements</li><li style="font-size: 10pt;">Contribute to automation of environment provisioning and dev workflows to enable fast iteration</li></ul>What We’re Looking For<ul><li style="font-size: 10pt;">2+ years in ML/AI or data-focused engineering or data science roles building production systems data or AI systems</li><li style="font-size: 10pt;">Demonstrated experience tuning LLM applications: prompt engineering, evals, retrieval, agent design, or similar</li><li style="font-size: 10pt;">Strong hands-on coding in Python or TypeScript (TypeScript familiarity a plus; willingness to work across the stack required)</li><li style="font-size: 10pt;">ML engineering mindset beyond notebooks: testing, CI, observability, performance, and deployment in production</li><li style="font-size: 10pt;">Comfort with SQL and complex data modeling; familiarity with data warehouses and pipelines</li><li style="font-size: 10pt;">Pragmatic, product-oriented approach—optimize for impact over novelty; complement existing systems rather than rebuild from scratch</li><li style="font-size: 10pt;">Ability to design experiments, quantify improvements, and communicate trade-offs clearly</li></ul>Nice to Have<ul><li style="font-size: 10pt;">Experience with text-to-SQL systems, semantic layers, or BI/analytics workflows</li><li style="font-size: 10pt;">Exposure to RAG frameworks, knowledge graphs, vector stores, and evaluation tooling</li><li style="font-size: 10pt;">Prior work in analytics engineering or data engineering environments</li></ul>Success Looks Like<ul><li style="font-size: 10pt;">Measurable improvements in text-to-SQL accuracy across target datasets and partners</li><li style="font-size: 10pt;">Reliable eval pipeline and regression suite running in CI to catch degradations</li><li style="font-size: 10pt;">Clear architecture and documentation for context/agent systems that others can contribute to</li><li style="font-size: 10pt;">Short feedback cycles with partners leading to fast, meaningful product wins</li></ul>Compensation<ul><li style="font-size: 10pt;">Competitive salary and meaningful equity</li><li style="font-size: 10pt;">Comprehensive benefits </li></ul>#LI-REMOTE<div class="content-conclusion">-Remote</div>

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