Posted today · be early

Staff Applied AI Engineer

StackBlitz

WorldwideremotePosted today

Skill Required

AI-EngineeringApplied-AIMachine-Learning-EngineeringSoftware-EngineerAI-Agent-DevelopmentStaff-Applied-AI-EngineerStaff-AI-EngineerSenior-Applied-AI-EngineerStaff-AI-Software-EngineerSr.-Staff-AI-EngineerStaff-ML-EngineerStaff-Machine-Learning-EngineerSenior-Applied-AI-Product-EngineerApplied-AI-ML-EngineerAI-Applications-EngineerAI-EngineerApplied-AI-EngineerAI EngineerAIsoftware engineeringGenerative AIMachine LearningEngineeringautomationNode.jsbuildingwrittendesignOpenAPIandGoFulltime

Key highlights

Remote-friendly (not limited to U.S.)
No college degree required
Staff Engineer level
Deep LLM production experience required

Role overview

Bolt.new by StackBlitz is a fully remote, globally distributed team building a next-gen, AI-powered app builder that enables users to create, edit, and deploy full-stack web and mobile apps instantly in the browser. As a Staff Engineer on the AI team, you'll lead the technical direction of the AI agents that turn natural language into production-ready applications, defining the patterns and systems that govern how AI reasons about and generates full-stack applications and influencing the broader AI strategy.

Responsibilities

Define AI Agent Architecture: Set technical direction for how agents manage context, orchestrate workflows, and scale.
Lead multi-model strategy: Build evals and selection criteria across providers (OpenAI, Anthropic, Google); partner with provider teams to test new capabilities.
Build tool-use & workflow foundations: Design safe, reliable interfaces for tool calling (search, queries, domain actions); evaluate frameworks (e.g., Vercel AI SDK, LangGraph) and set org best practices.
Drive cross-team execution: Align product/design/engineering, resolve tradeoffs, and mentor engineers to raise AI engineering standards.
Establish data & evaluation standards: Own dataset methodology and the eval harness; turn failure modes and conversation insights into measurable improvements.
Drive Research and Innovation: Experiment with prompting, context handling, and post-training; share learnings externally when appropriate.

Requirements

Deep LLM experience: Built and scaled production LLM systems; strong grasp of capabilities, limits, and emergent behavior.
Prompt engineering: Sets best practices and mentors across models and use cases.
Software engineering: Strong fundamentals; designs scalable systems and makes pragmatic architectural calls.
Strategic execution: Drives ambiguous, high-scope work end to end; influences across teams.
Systems thinking: Spots process/communication/technical debt and improves team velocity.
Model & agent literacy: Tracks coding-agent/LLM advances; understands model tradeoffs and the agent lifecycle.
Data-driven leadership: Builds data collection + eval harnesses; turns insights into measurable improvements.
Strong verbal and written English communication skills are required, as this role involves frequent collaboration with team members, stakeholders, and customers where English is the primary working language.

Nice to have

Fine-tuning and alignment of: LLMs (SFT, RLHF/RLAIF, DPO/ORPO)
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.
External Presence: Experience speaking at conferences, publishing technical content, or representing an organization in industry forums.