AI Engineer

November 18

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Bobsled

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SaaS • Cloud Computing • Enterprise

Bobsled is a company that offers a streamlined platform for sharing data across cloud environments, facilitating data integration and management for data providers and consumers. With a focus on reducing complexity in multi-cloud management and providing ready-to-query data, Bobsled allows users to connect to various data lakes or warehouses and share data efficiently with a single API. It supports multiple cloud destinations with a pay-as-you-go pricing model, making it easier for companies to deliver data products to customers without the need to manage complex pipelines. Bobsled is a tool for data companies and software companies aiming to modernize their data delivery systems and accelerate data analysis processes.

11 - 50 employees

☁️ SaaS

🏢 Enterprise

📋 Description

• 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

🎯 Requirements

• 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 • 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

🏖️ Benefits

• Competitive salary and meaningful equity • Comprehensive benefits