Senior AI/ML Engineer

Compensation: zł336k – zł420k • No equity

We are looking for a Senior AI Systems Engineer to join an ambitious European AI startup building a next-generation intelligence platform for business decision-making.

The product combines advanced reasoning systems, retrieval architectures, knowledge graphs, and AI agents to help organizations analyze complex information, evaluate opportunities, and make better strategic decisions. Rather than building another chatbot, the team is focused on solving some of the hardest challenges in AI today: memory, reasoning, retrieval quality, explainability, and long-context understanding.

This is an opportunity to work on cutting-edge AI systems alongside highly experienced engineers in an environment where experimentation, research, and rapid iteration are part of everyday work.

Responsibilities:

Build scalable memory and retrieval systems for high-context AI workflows Design hybrid retrieval architectures combining vector search, knowledge graphs, lexical search, and structured data Develop evaluation frameworks to benchmark retrieval quality, memory performance, and reasoning effectiveness Build agent memory interfaces that enable AI systems to query, inspect, and reuse context Define and improve quality metrics such as recall, provenance, latency, reliability, and cost efficiency Implement routing mechanisms that determine when to use retrieval, reasoning, or agent-based workflows Develop fine-tuning, distillation, and model adaptation pipelines Build observability and monitoring solutions for AI workflows, including retrieval failures, model regressions, and cost tracking Work closely with engineers and product leaders to continuously improve AI system quality and decision-making capabilities

Requirements:

5+ years of experience in ML Engineering, Applied AI, Search & Retrieval, or Backend AI Systems Strong Python development experience in production environments Hands-on experience with LLM-based systems, retrieval pipelines, embeddings, structured outputs, and agent orchestration Experience building evaluation frameworks, automated validation pipelines, golden datasets, or human feedback loops Understanding of retrieval failure modes such as hallucination amplification, ranking drift, stale context, and retrieval gaps Experience with Knowledge Graphs and graph-based memory systems (Neo4j or similar) Experience building APIs, asynchronous services, and production-grade backend systems Knowledge of observability, monitoring, schema validation, and debugging distributed systems Strong focus on measurable quality improvements rather than proof-of-concept development Experience using AI-native development tools such as Claude Code, Codex, Cursor, Gemini or similar

Nice to have:

・Experience with model fine-tuning, distillation, or local model deployment

・Experience working with agentic systems and long-term memory architectures

・Knowledge of sovereignty-driven model strategies

・Practical experience with graph, vector, lexical, and hybrid retrieval techniques

・Experience building AI products used in production by enterprise customers

Why join?

Work on advanced AI systems focused on reasoning, memory, and knowledge representation Help shape the architecture of a fast-growing AI product from an early stage Solve real-world AI challenges beyond traditional chatbot applications Influence technical decisions and product direction Join a highly technical team operating at the intersection of AI, retrieval systems, and decision intelligence