Forward Deployed Engineer, Trust and Safety
- Location
- Remote - USA
- Salary range
- USD $170,000 – $230,000 base
- Workplace
- Remote
- Level
- Individual contributor
- Job family
- Engineering
- Posted
- 2 months ago
The T&S Network estimates this role to be P4, Staff. The typical base salary for that level in Safety Engineering & Data Science is $150k to $200k at an employer this size for a remote role. Key skills at this level: owns a detection system end to end, including its drift and its retraining, designs for adversarial pressure rather than for a static distribution, and sets evaluation methodology others in the company adopt.
Why this level
Estimated from the posting rather than stated by the employer: asks for 8+ years · posts $170k to $230k base at a company under 500 for a remote role · the title reads as P2.
The role, in T&S terms
Written by the T&S Network from Sift's posting.
Fraud and trust and safety engineering for customers of an AI-powered fraud platform that scores events across a large data network. The role works on account takeover, chargebacks, approval-rate optimization and other abuse, and sits in a forward deployed team pairing customer-facing work with the Trust and Safety Architect and Data Science teams.
What you'd do
- Spot new fraud trends network-wide with the Architect and Data Science teams, then escalate and shut them down.
- Turn pattern findings into sharper signals, tighter configurations and smarter decisioning logic.
- Create dashboards and train models, decision logic and custom signals tailored to each vertical's customers.
- Dig into raw event streams to find false positives and coverage gaps, then test hypotheses and implement fixes.
- Lead forensic investigations during fraud spikes, tracing attack sources and writing up findings with remediation steps.
- Feed customer field observations into the internal roadmap and advocate for systemic product gaps.
What it asks for
- 5 to 8 years in fraud, trust and safety, risk or a closely related data science domain working with fraud data (required)
- Strong SQL and Python skills (required)
- Solid grasp of fraud-focused machine learning, including classifiers, feature building, tuning precision against recall, setting thresholds and spotting score drift (required)
- Experience analyzing large behavioral or transactional datasets for patterns and anomalies (required)
- Ability to present technical findings to technical and non-technical stakeholders (required)
- Customer-facing experience (required)
- Prior use of a fraud detection product, whether built in-house or bought from a vendor (preferred)
Worth knowing
- Some travel may be required
Our best summary of the posting, and it can be wrong. The employer's posting is what counts: read it in full on Sift before you apply. If we got something wrong, report this listing.
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Still listed 14 hours ago
About Sift
The employer
About Sift
Safety tooling · Fintech and payments · San Francisco, CA · 201 to 500 people · Founded 2011
Machine learning risk platform covering payment fraud, account abuse and content abuse.
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