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Senior Applied AI Engineer
Bubble PC
1 hour ago
Full-time
On-site
East New York, New York, United States
Requirements
You’ve shipped LLM-powered features in production You know what makes agents fail You’re comfortable moving across TypeScript and Python If that’s you, read on 5+ years of industry experience as a Software or Machine Learning Engineer, or a Master’s/PhD in AI, ML, or NLP with 3+ years of production experience A track record of shipping LLM-powered features in production—not just API integrations, but real systems with evals, monitoring, and iteration Hands-on experience building agentic AI workflows and debugging multi-step agent failures Strong command of RAG, context engineering, and retrieval pipeline design Experience with transformer models, embeddings, and AI model evaluation Proficiency in Python for AI/ML work and comfort working in a TypeScript/Node.js codebase Familiarity with tools like Hugging Face, LangChain, or Mastra Excellent communication skills and a collaborative, product-minded approach Alignment with our values: a desire to empower others, a focus on team and user success, and a willingness to experiment and learn from failures
What the job involves
We’re expanding our AI Engineering Team to build next-generation AI‑powered development workflows—enabling users to describe their applications in natural language and have AI generate, modify, and enhance their apps seamlessly As an Applied AI Engineer, you will help define and ship the future of AI‑powered development at Bubble This is an engineering role, not a research role You’ll own LLM‑driven product features end‑to‑end, design agentic workflows that real users depend on, and collaborate closely with Product, Infrastructure, and Research to push the limits of AI‑driven development Design and build agentic workflows that enable multi‑step AI‑driven app generation for real users at scale Improve LLM reasoning and retrieval techniques to enhance Bubble’s AI‑powered development tools Build and maintain production LLM pipelines—including prompt engineering, evaluation frameworks, and latency optimization Fine‑tune and optimize LLMs for AI‑assisted app‑building workflows using proprietary Bubble datasets Own AI feature quality end‑to‑end: from prototype through eval, deployment, and monitoring Work closely with the AI team to scale AI research into production systems
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You’ve shipped LLM-powered features in production You know what makes agents fail You’re comfortable moving across TypeScript and Python If that’s you, read on 5+ years of industry experience as a Software or Machine Learning Engineer, or a Master’s/PhD in AI, ML, or NLP with 3+ years of production experience A track record of shipping LLM-powered features in production—not just API integrations, but real systems with evals, monitoring, and iteration Hands-on experience building agentic AI workflows and debugging multi-step agent failures Strong command of RAG, context engineering, and retrieval pipeline design Experience with transformer models, embeddings, and AI model evaluation Proficiency in Python for AI/ML work and comfort working in a TypeScript/Node.js codebase Familiarity with tools like Hugging Face, LangChain, or Mastra Excellent communication skills and a collaborative, product-minded approach Alignment with our values: a desire to empower others, a focus on team and user success, and a willingness to experiment and learn from failures
What the job involves
We’re expanding our AI Engineering Team to build next-generation AI‑powered development workflows—enabling users to describe their applications in natural language and have AI generate, modify, and enhance their apps seamlessly As an Applied AI Engineer, you will help define and ship the future of AI‑powered development at Bubble This is an engineering role, not a research role You’ll own LLM‑driven product features end‑to‑end, design agentic workflows that real users depend on, and collaborate closely with Product, Infrastructure, and Research to push the limits of AI‑driven development Design and build agentic workflows that enable multi‑step AI‑driven app generation for real users at scale Improve LLM reasoning and retrieval techniques to enhance Bubble’s AI‑powered development tools Build and maintain production LLM pipelines—including prompt engineering, evaluation frameworks, and latency optimization Fine‑tune and optimize LLMs for AI‑assisted app‑building workflows using proprietary Bubble datasets Own AI feature quality end‑to‑end: from prototype through eval, deployment, and monitoring Work closely with the AI team to scale AI research into production systems
#J-18808-Ljbffr