Senior ML Engineer / Applied ML Engineer | NDA

Warsaw · 5+ years experience · Full-time · Hybrid · Posted Sep 28, 2026

At a glance

The Senior ML Engineer / Applied ML Engineer will build and evolve an internal entity resolution system for a global management consultancy.
The role focuses on applying machine learning, LLMs, and search algorithms to process and unify hundreds of millions of real-world data records.
This is a hands-on engineering position centered on developing production-ready ML systems and complex matching logic.

Design and improve entity matching, clustering, and deduplication algorithms at scale.
Implement distributed matching approaches including blocking strategies, multi-pass pipelines, and similarity-based methods.
Apply and combine rule-based, statistical, and ML-assisted techniques including embeddings.
Optimize candidate generation and scoring to balance accuracy, recall, performance, and cost.
Translate algorithmic ideas into scalable implementations using Spark and SQL.
Design, build, and operate a large-scale analytic system processing hundreds of millions to billions of records.
Implement and optimize Apache Spark pipelines for entity matching, deduplication, and clustering.
Build and maintain complex workflows using Airflow and DBT.
Develop and maintain analytical SQL models using Snowflake or similar cloud data warehouses.
Build validation logic and metrics to measure match rate, precision, recall, and accuracy.

5+ years of software development using Python, ideally in a team lead capacity.
Solid understanding of distributed systems and algorithms including partitioning, shuffles, joins, and scalability trade-offs.
Experience building and working with complex data pipelines or data systems.
Strong SQL skills, ideally with Snowflake or similar analytical databases.
Strong hands-on experience with Apache Spark (Databricks/PySpark or Scala) in production.
Experience with AI/ML-assisted systems such as embeddings, inference, and re-ranking.
Experience with or strong willingness to learn fuzzy and semantic matching techniques.
Understanding of Levenshtein distance, token-based similarity, BM25, vector embeddings, and vector search concepts.

Experience with data orchestration tools such as Airflow.
Experience building entity resolution, deduplication, or record linkage systems.
Familiarity with search or retrieval systems like Elasticsearch, OpenSearch, or vector databases.
Experience operating data pipelines at very large scale (100M+ records).
Background in data quality frameworks, validation automation, or QA at scale.
Experience with Kubernetes or Containerized Functions.
Experience with Serverless Functions.
Azure or other cloud experience and GitHub Actions.

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