Senior Software Engineer

Deep experience with ML techniques — supervised/unsupervised learning, anomaly detection, ensembles — applied to fraud or risk detection.

5+ years building production ML systems and scalable backend services.

Comfortable owning a model's full lifecycle in production — monitoring, retraining, and tuning as fraud patterns shift.

Fluent in Python and SQL; comfortable with ML frameworks (PyTorch, TensorFlow, scikit-learn) and a production language (Java, Go, or similar).

Experience with big data tools like Spark or Databricks for training and serving at scale.

Strong platform engineering skills: APIs, distributed services, and data pipelines built for scale and low latency.

Our compensation reflects the cost of labor across several  U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $139,000 -- $257,550 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.



In California, the pay range for this position is $177,900 - $257,550


At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans.  Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

State-Specific Notices:

Own the ML lifecycle end-to-end — data engineering, offline model development, and real-time inference — building the models that detect and stop fraud and abuse across our surfaces.

Turn behavioral, device, and transaction signals into features that sharpen detection.

Track precision, recall, false positives, and business impact, and experiment with new approaches (including LLMs and agentic AI) to catch more while reviewers do less.

Own the platform that serves those models in production, from APIs and SDKs to feature stores and the integration points that make onboarding new surfaces turnkey.

Keep it fast, reliable, and scalable as usage grows.

Partner with fraud investigators, Trust & Safety, and platform teams to turn new fraud patterns into new defenses, fast.

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