**Location:** 100% Remote (German Normal Employment Contract, International EOR, or Contractor)
**Employment Type:** Full-time
**Working Hours:** Flexible
**Working & Company Language:** English
**Start Date:** Immediately or as agreed

## About Us

We are a small, but growing SaaS technology startup building production-grade language understanding systems for **hotels and travel companies**. Our systems process real-world (inbound) emails and documents at scale and extract **structured, actionable data** from unstructured text.

This is **not a chatbot** and not a prompt-only project. Our platform performs **single-pass inference**, combining **deterministic rules, configuration-driven logic, and ML/NLP models** to reliably classify intent and extract information under real production constraints.

### Why this problem is interesting and hard

Hotel emails are deceptively complex. For example, a single booking-intent email may contain:

- Arrival and departure dates, or a length of stay
- Number of guests
- Room types that imply single or double occupancy
- Children and their ages, which must be evaluated against hotel-specific child policies to determine whether a child counts as an adult

One email may even contain **multiple booking requests**, sometimes across different hotels, which must be parsed and interpreted independently.

To further complicate matters, interpretation depends on **hotel-specific configuration**, such as:

- When a child is considered an adult
- When a request qualifies as a group booking (e.g., 7 rooms) versus an individual reservation

This creates real engineering trade-offs between **deterministic logic**, **ML-based inference**, and **LLM-supported components** — all while meeting strict requirements for accuracy, throughput, cost, and data protection.

### Where we are today

We run a **live production system** based primarily on **custom-trained RASA models**. Our current focus is on **improving overall Precision, Recall, and F1**, and evaluating whether newer model architectures can partially or fully replace existing components — without sacrificing speed, reliability, or commercial viability.

All systems run in **AWS infrastructure**, under strict **GDPR and performance requirements**.

Even though we’re technically deep, we’re also a **friendly, collaborative startup**. We have a strong error culture: **blame is boring and useless**. What matters is understanding *why* something happened and how, as a team, we can improve our systems so that **the next mistake is a new one — not the same one again**.

Our working and company language is **English**, and we collaborate daily in a fully international, remote-first setup.

## Your Role

As an **Applied NLP / Language Systems Engineer**, you will work on the **core language-processing pipelines** that power our product.

You will:

- Design, implement, and improve **intent recognition and structured information extraction pipelines**
- Build **single-pass inference systems** that combine deterministic logic, configuration data, and ML models
- Select, fine-tune, evaluate, and deploy **ML/NLP models** in production
- Measure, and improve **quality metrics** such as precision, recall, F1, and error rates
- Build evaluation pipelines and monitoring to ensure **stable production performance**
- Collaborate closely with backend engineers and product stakeholders to deliver reliable, measurable results

This is a hands-on role with **direct influence on architecture, modeling decisions, and evaluation methodology**.

## What We Are *Not* Looking For

- Prompt engineers
- Chatbot builders
- “API-only” AI integrations without systems understanding

We are looking for engineers who understand that ML and LLMs are **components in a larger system**, not magic black boxes.

## Requirements

### Must-Have

- Strong experience in **Applied NLP / Machine Learning**, with production system exposure
- Ability to design **hybrid pipelines** combining deterministic rules and ML models
- Solid understanding of **evaluation metrics** (precision, recall, F1) and error analysis
- Experience fine-tuning, deploying, or operating ML models in production environments
- Strong Python skills and experience with ML frameworks (e.g., PyTorch, TensorFlow)
- Awareness of **privacy, compliance, and operational constraints** (e.g., GDPR)

### Nice-to-Have

- Experience with **open-source LLMs** or encoder-only models in production
- Familiarity with **RASA**, spaCy, or classical NLP frameworks
- Experience with retrieval-augmented systems or vector-based approaches
- Cloud or infrastructure experience (AWS, GCP, Azure)

## What You’ll Get

- **100% Remote work** — work from anywhere
- **Flexible hours** — truly pick your own hours - outside of a meeting or two a week
- Direct impact on **core ML/NLP systems** and architectural decisions
- A **small, highly technical, pragmatic team** with flat hierarchies
- Modern technical environment and provided equipment (laptop, monitor if needed)
- A strong error culture: **blame is boring and useless**. We focus on learning, improving systems, and making sure we make **new mistakes — not repeat old ones**
- **Fair / Impact based Compensation **- honestly, we're not yet at the point where we can shower you with money, thoug we're not cheaping out either. But if you can drive us above 90% F1, then you'll have earned a massive raise, because your improvements will have helped generate the revenue to pay for it.
- **Base salary:** competitive for a senior Applied NLP/ML engineer across Europe (approx. €50k–85k/year), depending on experience and contract format.
**Performance-linked:** if you help push our F1 scores above 90%, you’ll earn a **significant raise (up to 50%)**, because your improvements generate the revenue to make it happen.

## Application

Please send:

- Your **CV / resume**
- Relevant **project examples or technical descriptions**
- A short note explaining **why you are interested in working on this particular problem. What is it that make that challenge interesting?**

📧 **Email applications to:** career@hotelresbot.com

For questions about the role or application process, feel free to reach out via email.