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AI Engineer – Remote Europe

AI Summary
Reedsy is seeking an AI Engineer to develop AI-powered authoring tools and improve workflows across its publishing product ecosystem. The role covers the full AI product lifecycle, from model selection and evaluation through scalable deployment and go-to-market execution. Key work includes LLM applications, retrieval-augmented generation, evaluation benchmarks, automated testing, and hallucination mitigation. The engineer will collaborate with product, engineering, and operations stakeholders to identify and deliver high-impact AI opportunities. This is a remote full-time position available in the United Kingdom, Poland, Spain, the Netherlands, Portugal, and France.
Remote from
UK, Spain, France+3 more, Netherlands, Portugal, Poland
Salary
EUR 70k–120k / yr
Employment
Full Time
Experience
Open level
Published
Apply before
11 Dec 2026
Listing views
31
Application actions
2
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Opportunity details

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

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior-level applied AI role requiring production ownership across modeling, evaluation, deployment, and product delivery. Success depends on strong practical judgment around LLM reliability, scalable cloud systems, and user-centered AI design.

Salary analysis

Estimated compensation compared with the broader EU market for similar roles.

Estimated job median (per year)Market rate
€95,000
EU market range (per year)€75k–€125k
AI insightThe employer explicitly lists a yearly salary range of EUR 70,000–120,000, with a midpoint of EUR 95,000. The estimated European market range for a production-focused AI Engineer is EUR 75,000–125,000 per year, varying by seniority and hiring country.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a retrieval-augmented generation feature for an author-facing writing assistant?

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.

What approach would you use to evaluate an LLM feature before release?

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.

Describe techniques for reducing hallucinations in a production LLM application.

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.

How do you decide whether to use prompt engineering, fine-tuning, or a different model?

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.

How would you collaborate with non-technical stakeholders to identify valuable AI opportunities?

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.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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