- 🚀 About Us
- We’re Bolt.new by StackBlitz!
- We’re the team that brought you WebContainers, the first-of-its-kind technology that made it possible to run Node.js right inside your browser. That breakthrough kicked off our journey in 2019, and it’s what powers the blazing-fast online IDE used by over 1 million developers every month.
- But we didn’t stop there.
- We doubled down on everything we learned and built Bolt.new — the fastest way to go from idea to production without writing traditional code. It’s a next-gen, AI-powered app builder that helps you create, edit, and deploy full-stack web and mobile apps instantly, right in your browser. No installs. No setup. Just smart automation and instant dev environments that let you move at the speed of thought.
- We’re a fully remote team, globally distributed, deeply collaborative, and seriously passionate about building the future of software development.
- This is your chance to join a small team with a big vision. 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
- 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. 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 across multiple teams and influencing 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: 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.
- 💡 Qualifications
- 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.
- 🎯 Bonus Points
- 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.
- 📌 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