Staff Machine Learning Engineer (AI)

TL;DR

Staff Machine Learning Engineer (AI/RAG): Building and scaling AI-powered search capabilities and RAG bots for a global customer experience platform with an accent on hybrid search, LLM integration, and high-scale retrieval systems. Focus on optimizing indexing pipelines, enhancing search ranking, and designing scalable distributed architectures.

Location: Hybrid in Krakow, Poland

Salary: zł392,000.00-zł588,000.00

Company

A global leader in customer experience software helping over 145,000 brands manage billions of customer interactions.

What you will do

• Deliver AI-powered capabilities using the latest LLM technologies at global scale.
• Design and improve hybrid search solutions combining vector embeddings and keyword-based retrieval.
• Collaborate with Product Management and ML Scientists to define feature scope and implementation strategies.
• Optimize indexing pipelines for speed and cost-efficiency and enhance search result ranking.
• Mentor junior team members and contribute to technical design and best practices.
• Ensure high stability and reliability of deployed services through clean, maintainable code.

Requirements

• Proficiency in Python or Ruby with experience in relevant testing frameworks.
• Solid understanding of architecture and software design patterns for server-side and web applications.
• Proven experience building scalable and stable software applications.
• Ability to formulate hypotheses, conduct experiments, and analyze results to drive engineering decisions.
• Must be based in or be able to work from the Krakow, Poland office (Hybrid).

Nice to have

• Experience implementing and optimizing search solutions using Machine Learning and Elasticsearch.
• Experience managing and deploying cloud services within AWS.
• Experience with event-driven distributed architecture using Kafka.

Culture & Benefits

• Opportunity to work on the cutting edge of RAG and Natural Language Processing.
• High level of ownership over product features impacting millions of users.
• Hybrid work model offering a balance between in-person collaboration and remote flexibility.
• Professional growth paths with possibilities to specialize in security, performance, or reliability.