Senior Applied AI Engineer

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 Senior Engineer on the AI team, you’ll build and improve the core coding AI agents that power Bolt.new. You’ll collaborate directly with product, design, engineering, and data to solve key agent challenges: evaluation harnesses, context compression, maintaining context across large codebases, orchestrating intuitive multi-step workflows, and tackling everything from simple UI tweaks to complex architectural decisions.

This isn't API integration work. You'll be contributing to how AI reasons about and generates full-stack applications, working alongside experienced engineers in a problem space where the patterns are still being invented. Your work ships to millions of users building real products with Bolt.new every day.

How You'll Contribute

• Build and ship state-of-the‑art coding agents for Bolt.new (long‑running workflows, context compression, agent loops, model routing).

• Own agent capabilities end‑to‑end: planning, tool use, code generation/editing, and maintaining context across large codebases and multi‑turn sessions.

• Integrate and evaluate multiple LLM providers (OpenAI, Anthropic, Google): benchmark quality/cost/latency; choose the right model for each task; and adopt new model features.

• Design safe, reliable tool‑use and workflow orchestration (e.g., Vercel AI SDK, LangGraph): structured actions, guardrails, and production observability.

• Partner closely with product/design/engineering/data to deliver robust AI features; mentor and raise the bar for peers.

• Build and maintain evaluation harnesses and datasets; analyze failure modes and user feedback to continuously improve quality and reliability.

Qualifications

• LLM Experience: Have shipped LLM agents to production environments used by thousands of users.

• Prompt Engineering: Deep understanding of prompt engineering best practices to guide LLM behavior. Able to craft, refine, and optimize prompts for different tasks and models.

• Software Engineering Skills: Solid software engineering fundamentals with experience in building production‑ready systems.

• Autonomous Execution: Ability to deliver high‑quality work with minimal oversight. Comfortable owning tasks end‑to‑end, managing complexity, and driving projects to completion within your functional area.

• Problem‑Solving: Strong analytical and problem‑solving skills with the ability to debug complex AI behaviors and bring clarity to ambiguous tasks.

• Data‑Driven Mindset: Comfortable collecting, curating, and analyzing data to inform decisions. Able to build datasets, identify patterns in agent behavior, and translate findings into actionable 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

• 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.

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