Software Development Engineer 3 - Graph Engineering

About the Role

Adobe is seeking a passionate engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide.

In this role, you will compose and develop our unified graph along with the detection systems running on it. You will merge multiple isolated fraud graphs into scalable source that identifies non-genuine and abusive signals across hundreds of millions of users

• You will manage the entire lifecycle: starting from raw behavioral and account data, progressing through large-scale graph modeling and ingestion using Databricks and Spark, applying Graph Data Science algorithms and graph ML, and delivering production-ready detection for enforcement.

This role is for an engineer eager to manage graph systems entirely, covering schema, pipelines, community detection, and graph ML rather than using pre-built solutions.

Key Responsibilities

• Build and evolve a unified graph schema that merges multiple fraud and account data sources into consistent, rebuildable model.
• Develop and enhance large-scale graph ingestion and feature pipelines on Databricks and Spark, converting raw behavioral and account events into refined graph nodes, edges, and properties.
• Apply Graph Data Science (GDS) algorithms including community detection (WCC, Louvain, Label Propagation), centrality (PageRank), and node embeddings (FastRP, Node2Vec) to identify abuse rings, shared-entity clusters, and coordinated fraud.
• Develop graph machine learning models, including Graph Neural Networks (GNNs), to extract high-value risk signals from network structure, and incorporate them into downstream ML workflows.
• Translate prototypes into production graph systems that are scalable, reliable, and observable, and drive query and inference performance through modeling and serving-side optimization.
• Own operational health of the graph platform: incremental refresh, supernode handling, monitoring, and cost efficiency at scale.
• Contribute to MLOps and data-engineering practices: pipeline orchestration, versioning, CI/CD, automated retraining, and production monitoring.
• Collaborate cross-functionally with data science, product, and platform teams; experimentation rigor, and responsible AI.
• Stay current with advances in graph ML and network science and bring relevant innovations into Adobe's products.

Minimum Qualifications

• Bachelor's degree or graduate degree or equivalent experience in Computer Science, Machine Learning, Data Science, or related field.
• 6+ years of professional experience building and deploying data or ML solutions at scale.
• Solid programming skills in Python, with practical experience developing extensive data pipelines on Databricks and Spark.
• Practical experience working with graph platforms such as Neo4j, Amazon Neptune, TigerGraph, or Memgraph, along with applying the Graph Data Science (GDS) library.
• Practical experience implementing GDS algorithms like PageRank, Louvain/Label Propagation for community detection, or Node Embeddings (FastRP, Node2Vec) to extract insights from network data.
• Comprehensive knowledge of the entire data and ML lifecycle, covering ingestion, feature engineering, deployment, and monitoring.
• Strong grasp of data modeling, query optimization, and production system integration.

Preferred Qualifications

• Experience in applying Graph Neural Networks (GNNs) and connecting GDS pipelines with downstream machine learning workflows.
• Experience in fraud detection, anomaly detection, or behavioral modeling.
• Familiarity with Neo4j Aura or other managed graph databases, and with large-scale incremental graph refresh and supernode management.

About Adobe

Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.

Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.

Let’s Adobe together

At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture, focus on people, purpose and community, Adobe for All, comprehensive benefits programs, the stories we tell, the customers we serve, and how you can help us advance our mission of empowering everyone to create.

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.

Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email [email protected].

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• Bachelor's degree or graduate degree or equivalent experience in Computer Science, Machine Learning, Data Science, or related field.
• 6+ years of professional experience building and deploying data or ML solutions at scale.
• Solid programming skills in Python, with practical experience developing extensive data pipelines on Databricks and Spark.
• Practical experience working with graph platforms such as Neo4j, Amazon Neptune, TigerGraph, or Memgraph, along with applying the Graph Data Science (GDS) library.
• Practical experience implementing GDS algorithms like PageRank, Louvain/Label Propagation for community detection, or Node Embeddings (FastRP, Node2Vec) to extract insights from network data.
• Comprehensive knowledge of the entire data and ML lifecycle, covering ingestion, feature engineering, deployment, and monitoring.
• Strong grasp of data modeling, query optimization, and production system integration.
• Experience in applying Graph Neural Networks (GNNs) and connecting GDS pipelines with downstream machine learning workflows.
• Experience in fraud detection, anomaly detection, or behavioral modeling.
• Familiarity with Neo4j Aura or other managed graph databases, and with large-scale incremental graph refresh and supernode management.