Suggested rewrite: Led a cross-functional initiative that improved [business outcome] by [measurable result], demonstrating experience relevant to this role...
AI Engineer – Remote Europe
- Salary
- EUR 70k–120k / yr
- Department
- Software Engineering
- Employment
- Full Time
- Experience
- Open level
- Published
- Apply before
- 11 Dec 2026
- Listing views
- 31
- Application actions
- 2
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About this role.
About Reedsy
We’re here to give authors the tools and resources they need to create beautiful books. Our marketplace gathers the industry’s best publishing professionals — the likes of Neil Gaiman’s editor, Nora Roberts’ book marketer, and GRRM’s cover designer.
We’ve grown to a community of 5,000,000 authors, and transformed the way people write and publish their book. Want to hop on board and help us get to our next destination? Cool, keep reading!
Responsibilities
At Reedsy, our goal is to build the best ecosystem for writers, supporting them at every stage of their creative journey. Over the years, we’ve developed a suite of products, including a marketplace, book discovery platform, writing app, central authentication system, and email marketing tools.
As a publishing startup, we’re thoughtful about how we integrate AI. As an AI Engineer, you’ll help us empower authors to do more — enhancing creativity, not replacing it. You’ll focus on building intelligent features that support storytelling and streamline workflows.
In this role, you will:
Design and implement AI-powered tools for authors (e.g. a narrative co-pilot, visual storyboarding assistant, etc.);
Improve internal workflows by automating repetitive tasks and enhancing UX across our products;
Build internal tools for AI development, including evaluation benchmarks and automated testing;
Collaborate closely with engineers and operations teams to identify high-impact AI opportunities.
Requirements
Proven experience integrating AI/ML into real-world products, with ownership of the full lifecycle — from modeling and evaluation to deployment and go-to-market execution.
Experience working with large language models (e.g., prompt engineering, fine-tuning, model selection, retrieval-augmented generation)
Familiarity with orchestration tools (e.g., LangChain, LlamaIndex, or custom chaining frameworks)
Knowledge of hallucination detection and mitigation strategies
Hands-on experience with cloud platforms (preferably Google Cloud Platform) and scalable AI deployment
Familiarity with embeddings, vector databases, and Elasticsearch
Experience with AI developer tooling (e.g., Cursor, intelligent CI/CD, AI-powered QA)
Bonus Points
Fluent in multiple languages. Reedsy welcomes applicants of all cultures and backgrounds.
A good dose of ambition, a willingness to learn, and a great sense of humor
Benefits
Work from home
Paid paternal and maternal leave
Annual team retreat
Salary range:
70k – 120k EUR
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Role DNA
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Core skills
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Sample interview questions
I would begin by defining the user task and the approved knowledge sources, then build an ingestion pipeline that chunks, embeds, and indexes content with metadata. I would evaluate retrieval quality separately from generation quality, use citations or source previews where appropriate, and add safeguards for unsupported claims and sensitive content.
I would create a representative benchmark set from real user scenarios, including edge cases and failure modes. I would measure task success, groundedness, latency, cost, and safety, combine automated scoring with expert review, and establish release thresholds and regression testing in CI/CD.
I would use constrained prompts, high-quality retrieval, clear instructions to abstain when evidence is unavailable, structured outputs, and post-generation validation. I would also monitor production feedback and errors so that benchmark coverage and retrieval quality improve over time.
I would first establish a baseline with prompt engineering and retrieval because they are usually faster to iterate and easier to update. I would consider fine-tuning only when there is a stable, well-defined task with sufficient high-quality examples and measurable improvement over the baseline. The final decision should balance quality, cost, latency, maintainability, and privacy requirements.
I would start with their workflow, pain points, and desired outcome rather than proposing a model immediately. I would translate opportunities into small, measurable experiments, communicate limitations and risks plainly, and use user feedback and operational metrics to decide whether to iterate, scale, or stop.
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