Bolt.new - Staff Applied AI Engineer
Requirements
• TypeScript: Familiarity with TypeScript is important. Our entire stack is built on it. Willingness to work in TS daily is key. • TypeScript: • 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. • Prompt Engineering: • 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. • Strategic Execution: • Systems Thinking: Ability to identify process, communication, and technical debt across the organization and propose solutions that accelerate velocity for multiple teams. • Systems Thinking: • 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. • Data-Driven Leadership: • 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. • DSPy Framework: Familiarity with DSPy (Declarative Self-improving Python) for building modular AI systems and optimizing prompts programmatically. • DSPy Framework: • Machine Learning Background: Understanding of ML fundamentals and experience with model evaluation metrics. • Machine Learning Background: • Open Source Contributions: Experience contributing to or maintaining open-source AI/ML projects. • Open Source Contributions: • Research Background: Experience reading and implementing techniques from AI/ML research papers. • Research Background: • $ 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
Responsibilities
• 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. • 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. • 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 for integrating these tool interactions into our workflows. • Drive initiatives across multiple teams by defining patterns and systems governing how AI reasons about and generates full-stack applications. Influence the broader AI strategy of Bolt through technical direction in shaping user experiences with natural language interfaces to build real products instantly within browsers.
Benefits
• Fully remote work environment with globally distributed team • Opportunity for leading technical direction in AI agent development • Influence on broader company's AI strategy and multi-provider model approach • Ability to shape user experience through defining patterns, frameworks, and standards
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