Forward Deployed Engineer (Trust and Safety)

Join Sift, a company dedicated to making the internet a safer and more trusted place. As a Forward Deployed Engineer in Trust and Safety, you will work closely with our Trust and Safety Architect and Data Science teams to detect and act on various types of online abuse. You will analyze large-scale behavioral and transactional datasets, communicate technical findings to both technical and non-technical stakeholders, and contribute to detection frameworks and investigative tooling. This role requires 5-8 years of experience in fraud, trust & safety, risk, or a closely related technical domain, as well as strong SQL and Python skills.

• Collaborate with Trust and Safety Architect and Data Science teams to identify and address emerging fraud patterns.
• Lead forensic investigations during fraud spikes, tracing attack patterns to their source and delivering clear remediation steps.
• Contribute to detection frameworks, investigative tooling, and internal playbooks to enhance the effectiveness of the team.
• Customer-facing experience; you understand that different businesses have different priorities, and that listening before optimizing is part of the job
• 5–8 years in fraud, trust & safety, risk, or a closely related technical domain - you've spent meaningful time working with fraud data, not just adjacent to it
• Experience analyzing large-scale behavioral or transactional datasets to find patterns and anomalies - you know what a fraud ring looks like in the data, not just in a textbook
• Ability to communicate technical findings to both technical and non-technical stakeholders; you can write a forensic investigation report and present it to a VP of Risk in the same week
• Strong understanding of ML concepts applied to fraud: classification models, feature engineering, precision/recall tradeoffs, threshold calibration, score drift
• Strong SQL and Python skills; you reach for code to answer a question, not to build a pipeline
• Hands-on experience with fraud detection platforms (in house or 3rd party)
• Hands-on experience building with AI: LLM APIs, prompt engineering, or agentic workflows - whether that's automating an investigation step, building a tool that surfaces patterns from raw data, or wiring together a multi-step agent to accelerate fraud analysis
• Familiarity with real-time event processing systems
• Experience with rules-based decisioning systems alongside ML - knowing when a hard rule beats a model score
• Background in payments, e-commerce, fintech, marketplace, or account security fraud
• Prior forward deployed, staff engineering, or embedded consulting experience at a technical product company
• Computer Science, Mathematics, Statistics, Information Systems, Economics degree or equivalent