Machine Learning Specialist

BnBerry; España; A distancia; EUR 40.000 - 70.000; Jornada completa

Descripción de la vacante

A leading company in the rental and housing industry is seeking a Machine Learning Engineer to work on an innovative travel-tech project. The role involves building production systems using LLMs, with strong focus on backend engineering and user behavior analysis. Candidates should have proven experience with modern ML frameworks and the ability to work in a fast-paced environment.

Servicios

Fully remote setup

Direct impact on product development

Fast growth environment

Ownership in decision making

Flat team structure

Formación

• Proven experience building production systems around LLMs, especially fine-tuning and prompt engineering.
• Strong backend engineering skills in Python (FastAPI or similar).
• Practical experience with vector databases like Pinecone, Weaviate, Qdrant, or FAISS.

Responsabilidades

• Design and implement machine learning systems that will be put into production.
• Analyze user behavior to extract valuable insights and improve ranking signals.
• Build dynamic data pipelines for real-time machine learning applications.

Conocimientos

Machine Learning

Backend Engineering in Python

Vector Databases

A/B Testing

Computer Vision

Descripción del empleo

BnBerry has been shaping the rental and housing industry for more than a decade. Now we are launching a disruptive travel-tech project where machine learning is the core engine, not a side tool.

We are looking for driven minds — people who live and breathe machine learning, who learn and build fast and who already proved it by building working projects, startups, or ambitious personal experiments.

Requirements

• Proven experience building production systems around LLMs, especially fine-tuning and prompt engineering.
• Strong backend engineering skills in Python (FastAPI or similar), with ability to build service layers and interface with external APIs (e.g. OpenAI).
• Practical experience with vector databases like Pinecone, Weaviate, Qdrant, or FAISS — including document ingestion, indexing, and query ranking.
• Experience tracking and analyzing user behavior / feedback to extract ranking signals and patterns.
• Familiarity with embedding models, chunking strategies, and hybrid search logic.
• Ability to design and implement lightweight data pipelines for dynamic preference updates and context generation (e.g. Pandas, DuckDB, Airflow, etc.).
  
  

Bonus Skills (Highly Valued)

• Applied experience with computer vision: image classification, detection, segmentation, generative models.
• Experience combining vision and language models in real-world applications.
• Understanding of LLM fine-tuning and transfer learning (even if not used in this project).
• Experience with fast experimentation: A/B testing, ranking evaluation, or online learning approaches.
  
  

Who We're Looking For

• You are passionate about ML and keep up with the latest research and frameworks.
• You build fast — startup prototypes, hackathon projects, ambitious personal work — and you ship.
• You are not buried in legacy stacks. Your skills are fresh, modern, and relevant.
  
  

What We Expect From Your Application

• A short CV or summary about yourself.
• Description of ML project(s) you've worked on: scope, stack, and results.
• Bonus: link to GitHub, portfolio, or personal site.
  
  

Benefits

• Work on a revolutionary product at the intersection of travel and machine learning.
• Direct impact: your models go to production and shape the core of the product, not stay in research slides.
• Fast growth environment: exposure to modern ML stacks (transformers, multimodal ML, computer vision) with constant room to experiment.
• Freedom & flexibility: fully remote setup, no corporate bureaucracy, focus on output not hours.
• Ownership: you'll have autonomy in decision-making and the chance to influence product direction.
• Flat team structure: work directly with founders and senior engineers, no endless management layers.
• Visibility: your contributions will be recognized, not lost in a big company hierarchy.

Nivel de antigüedad

Intermedio

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