Software Engineer with ML and Data Skills

Our client is a NASDAQ-listed B2B data company powering Go-To-Market strategies with a 360-degree view of every customer, a view whose value depends on the quality of billions of person and company records.

 Anomalsky is the ML system built to catch what traditional observability misses: row-level semantic anomalies (e.g., a first_name, title, company_name). Three layers, an ML layer (embeddings + unsupervised clustering) flags suspicious records at scale, an LLM layer removes false positives and explains each cluster, and an optional human-in-the-loop lets domain experts resolve whole clusters at once. The MVP already drove ~40k crucial record corrections in production. 

What’s next: the MVP is landing on GCP now. Once it’s operational, the mission is to scale Anomalsky across the entire organization, embedding it into Acquisition pipelines and building a real-time variant that scans data before it reaches customers.

• Productionizing Anomalsky on GCP and scaling it to operational, organization-wide use.
• Evolving the ML / LLM / human-in-the-loop design and the feedback loop that turns expert reviews into reusable knowledge.
• Prototyping the low-latency real-time variant.
• Integrating Anomalsky into existing workflows, starting with Acquisition.

Python, Airflow, BigQuery, Snowflake, Spark (Dataproc), Databricks, Iceberg, Starburst, Trino, AWS, GCP, Docker, Terraform, Jenkins, GitHub, Scikit Learn,  unsupervised anomaly detection (kNN, Isolation Forest, autoencoders), recursive clustering, classifiers on real + synthetic data, MLflow, LLM-based reasoning.

ML and data engineers from VirtusLab collaborating with customer data engineers and product management.