Machine Learning Specialist

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.

• 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.).

• 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.

• 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.

• 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.

• 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.