Software Development Engineer 3 - Graph Engineering
Core
Build and evolve a unified graph schema and detection systems to protect Adobe's ecosystem from fraud, abuse, and misuse by merging isolated fraud graphs into one scalable source.
Role type
Senior IC Graph Engineering & Machine Learning Engineer
Builds
Scalable production graph systems, ingestion pipelines, and detection models for fraud enforcement
Domain
Cybersecurity / Fraud Detection / Graph Data Science
Deliverable
production ML models
Required skills
Python, Databricks, Spark, Graph Data Science (GDS) algorithms, Graph Neural Networks (GNNs), data modeling, query optimization, MLOps
Preferred skills
Fraud detection, anomaly detection, behavioral modeling, managed graph databases (Neo4j Aura), large-scale incremental graph refresh
Technologies
Databricks, Spark, Neo4j, Amazon Neptune, TigerGraph, Memgraph, GDS library, FastRP, Node2Vec, Louvain, PageRank
Responsibilities
Build unified graph schema merging multiple data sources; Develop large-scale graph ingestion and feature pipelines; Apply GDS algorithms for community detection and node embeddings; Develop graph ML models (GNNs) for risk signals; Translate prototypes to production systems; Own operational health of the graph platform; Contribute to MLOps and data-engineering practices
Seniority
Senior, hands-on IC
