Staff Applied AI Engineer

🚀 About Us We’re Bolt.new by StackBlitz! We're the team behind WebContainers, the groundbreaking technology that made it possible to run Node.js right in your browser. No installs, no setup, just instant dev environments. That innovation kickstarted our journey in 2019 and powers the blazing-fast online IDE used by over a million developers every month. But we didn't stop there. We took everything we learned from building WebContainers and used it to create Bolt.new. The fastest way to go from idea to production without writing traditional code. Think of it as the Canva or Figma for full-stack applications: a next-gen, AI-powered builder that lets you create, edit, and deploy web and mobile apps instantly, right in your browser. WebContainers make it possible. Bolt.new brings it to life. Together, they reimagine what it means to build software lowering the barrier to entry, speeding up workflows, and unlocking creativity for the next generation of builders. We're a globally distributed, fully remote team of passionate engineers, designers, and creatives building the future of software development. If you love shipping fast, solving real problems, and pushing the boundaries of what's possible we'd love to meet you. ✨ About This Opportunity You'll lead the technical direction of the AI agents that turn natural language into production-ready applications. This means shaping how we work with LLMs to solve our hardest problems: maintaining context across large codebases, orchestrating multi-step workflows that feel intuitive, and handling everything from simple UI tweaks to complex architectural decisions. This isn't API integration work. You'll define the patterns and systems that govern how AI reasons about and generates full-stack applications, driving initiatives that span multiple teams and influence our broader AI strategy. Your work shapes the experience of millions of users building real products with Bolt every day. 🛠️ How You'll Contribute Define AI Agent Architecture: Lead the design and evolution of our AI agent systems, establishing patterns, frameworks, and standards that teams across the organization adopt. Own the technical vision for how agents manage context, orchestrate workflows, and scale to handle increasingly complex user needs. Drive Multi-Provider Strategy: Shape our approach to leveraging models from providers such as OpenAI (GPT series), Anthropic (Claude), and Google (Gemini). Establish evaluation frameworks and selection criteria that teams use to choose the right model for a given task. Build relationships with provider teams to influence roadmaps and beta-test new capabilities. Architect Tool Use and Workflow Systems: Design the foundational systems that enable AI agents to call external tools and APIs safely and effectively. Define the abstractions and interfaces that allow the agent to perform actions like web searches, database queries, and domain-specific operations. Evaluate and recommend frameworks such as Vercel's AI SDK, LangGraph, and others, establishing best practices for the organization. Cross-Team Leadership: Partner with teams across engineering, product, and design to align AI initiatives with business objectives. Influence roadmaps, resolve technical disagreements, and ensure AI-driven features are architected for long-term maintainability and performance. Mentor senior and mid-level engineers, raising the bar for AI engineering practices across the organization. Establish Data and Evaluation Standards: Define the methodology for collecting, curating, and analyzing datasets from agent responses and multi-turn conversations. Build and steward the evaluation harness, ensuring evals directly support business objectives and KPIs. Turn insights from conversation patterns, failure modes, and success signals into systematic improvements. Drive Research and Innovation: Stay at the forefront of NLP and LLM research, identifying and championing novel techniques that provide competitive advantage. Lead experimentation with new prompting strategies, context handling methods, and fine-tuning opportunities. Represent StackBlitz in external forums, conferences, and community discussions where appropriate. 💡 Qualifications TypeScript: Familiarity with TypeScript is important. Our entire stack is built on it. Willingness to work in TS daily is key. Deep LLM Experience: Extensive hands-on experience working with Large Language Models (LLMs), with a nuanced understanding of their capabilities, limitations, and emergent behaviors. Proven track record of building and scaling production AI systems. Prompt Engineering: Deep expertise in prompt engineering with the ability to establish best practices and mentor others. Skilled at crafting, refining, and optimizing prompts across different tasks, models, and use cases. Software Engineering Excellence: Strong software engineering fundamentals with experience designing systems that scale. Able to make architectural decisions that balance immediate needs with long-term maintainability. Strategic Execution: Ability to take ambiguous, high-scope problems and drive them to completion with minimal oversight. Comfortable influencing direction across teams and navigating complex technical and organizational challenges. Systems Thinking: Ability to identify process, communication, and technical debt across the organization and propose solutions that accelerate velocity for multiple teams. Data-Driven Leadership: Experienced in establishing data collection and analysis practices. Able to build evaluation frameworks, identify patterns in agent behavior, and translate findings into organizational improvements. Strong verbal and written English communication skills are required, as this role involves frequent collaboration with team members, stakeholders, customers, and potentially external audiences where English is the primary working language. 🎯 Bonus Points DSPy Framework: Familiarity with DSPy (Declarative Self-improving Python) for building modular AI systems and optimizing prompts programmatically. Machine Learning Background: Understanding of ML fundamentals and experience with model evaluation metrics. Open Source Contributions: Experience contributing to or maintaining open-source AI/ML projects. Research Background: Experience reading and implementing techniques from AI/ML research papers. Experience speaking at conferences, publishing technical content, or representing an organization in industry forums. 📌 A Few Notes You do not need a college degree to apply You do not need to be located in the U.S. — we’re remote-friendly You do not need to meet every qualification listed above