sift - Forward Deployed Engineer, Trust and Safety
Requirements
• 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 • Strong SQL and Python skills; you reach for code to answer a question, not to build a pipeline • Strong understanding of ML concepts applied to fraud: classification models, feature engineering, precision/recall tradeoffs, threshold calibration, score drift • 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 • Customer-facing experience; you understand that different businesses have different priorities, and that listening before optimizing is part of the job • 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 • Let’s build it together: • At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community – ultimately using this empowerment and authenticity to build trust and create a safer Internet. • This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
Responsibilities
• Work with our Trust and Safety Architect and Data Science teams to surface emerging fraud patterns across the network escalate and proactively take them down. • Detect patterns and turn those findings into sharper signals, tighter configurations, and smarter decisioning logic. • Work across different verticals and closely with customers, partners and prospects with different risk appetites - some optimizing for approval rates, some minimizing chargebacks, some fighting account takeover and other types of abuse. • Help build dashboards, tune models, decision logic and custom signals to help customers achieve their desired business outcomes • Identify sources of false positives, possible coverage gaps and other vulnerabilities by digging into raw event streams; form a hypothesis, design a test and implement the fix • Lead forensic investigations during fraud spikes: trace attack patterns to their source, identify the technique being used, deliver a clear writeup with remediation steps • Distinguish between one-off anomalies and systemic gaps that indicate a product opportunity - and advocate for the latter with rigor • Contribute to detection frameworks, investigative tooling, and internal playbooks that make every engineer and analyst at Sift more effective • Be the conduit between customer reality and internal roadmap; your field observations should directly accelerate what Sift ships next
Benefits
• Estimated On-Target Earnings $170K – $230K • Offers Equity • The anticipated starting on-target earnings range for this role is listed above. This role may also be eligible for equity and other benefits. Sift takes into account many factors in determining compensation, including level, location, transferable skills, work experience, business needs, and market demands. Therefore, the pay range / final offer may change or be modified in the future. • on-target earnings range • Legal first and last name • or drag and drop here • Please list the location where you expect to work. • Yes, I will require visa sponsorship for employment with Sift now or in the future • No, I will not require visa sponsorship for employment with Sift now or in the future • Select all that apply • American Indian or Alaskan Native • Black or African American • Hispanic or Latinx • Native Hawaiian or Other Pacific Islander • Southeast Asian • I prefer not to say • A race, ethnicity, or origin not shown • My gender identity is not listed • They/Them/Theirs • Prefer not to say • I am not a protected veteran • I identify as one or more of the classifications of a protected veteran • Prefer not to say • Yes, I have a disability or previously had a disability • No, I don't have a disability • Prefer not to say • This document provides transparency around the way in which Sift handles personal data of job applicants: https://sift.com/legal-and-compliance/sift-global-recruitment-notice • Yes, I acknowledge Sift’s Global Recruitment Privacy Notice • Recruiting Privacy Policy
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