Senior Software Engineer, Fraud

aijoblist

Foster City (CA)

On-site

USD 120,000 - 160,000

Full time

14 days+

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Benefits offered by this job

Competitive Salary & Equity

401(k) Program with a 4% match

Health, Dental, Vision and Life Insurance

Flexible Time Off (FTO)

Commuter Benefits

Wellness Stipend

Job summary

Replit is seeking a Security Engineer to join their Fraud team in Foster City, CA. You will design and implement detection systems for AI-generated code, analyze attack patterns, and work on unique security problems. This full-time role involves collaborating closely with various teams and has an in-office requirement of three days a week.

Ideal candidates will have over 4 years of experience in security engineering and strong programming skills in Python or TypeScript. Benefits include competitive salary, 401(k) program, health insurance, and more.

Qualifications

4+ years of experience in security engineering, anti-abuse, or fraud detection.
Strong programming skills in Python and/or TypeScript.
Experience with SQL and data analysis at scale.

Responsibilities

Design and implement LLM guardrails for AI-generated code.
Build AI-powered detection systems for malicious patterns.
Analyze attack patterns using BigQuery and Hex.

Skills

Security engineering

Python programming

Data analysis

Machine learning

Communication skills

Tools

BigQuery

SQL

GCP

Kubernetes

Job description

Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation.

About the role

The Fraud team is the front line defending Replit's platform from exploitation. We detect and shut down phishing deployments, prevent cryptomining on free-tier infrastructure, stop LLM token farming, and keep bad actors from weaponizing the platform against our users. This is adversarial work: attackers adapt constantly, and we build the detection systems, heuristics, and automated responses that stay ahead of them.

What makes this role unique is the AI-native nature of Replit's platform. You'll work on problems that barely exist elsewhere: building guardrails for AI-generated code, detecting prompt injection attacks at scale, and using LLMs as a defensive tool against abuse. If you want hands‑on experience applying AI to security problems, this is one of the few places you can do it in production with real attackers. You'll own problems end-to-end, from identifying emerging abuse patterns to shipping the systems that stop them at scale.

In this role you will…

Design and implement LLM guardrails that detect abuse scenarios in AI-generated code and agent interactions
Build AI-powered detection systems that use LLMs to identify malicious patterns, classify threats, and automate response decisions
Build and operate abuse detection systems that identify phishing, cryptomining, account takeover, and financial fraud across millions of daily user actions
Design automated response mechanisms that enforce platform policies without manual intervention
Own the full abuse response lifecycle: detection, investigation, enforcement, and handling appeals alongside Support and Legal
Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection rules
Maintain and extend internal detection tools (Slurper, Netwatch) that continuously monitor user activity
Integrate and tune security scanners (SAST, SCA) in CI pipelines with tight performance SLAs
Track abuse trends, measure detection effectiveness, and adapt defenses as attack patterns evolve

Required skills and experience

4+ years of experience in security engineering, anti-abuse, trust & safety, or fraud detection
Strong programming skills in Python and/or TypeScript for building detection systems and automation
Experience with SQL and data analysis at scale (BigQuery, Snowflake, or similar)
Experience building or fine-tuning ML/LLM-based classifiers for security or abuse detection
Familiarity with prompt injection, jailbreaking, and other LLM-specific attack vectors
Ability to investigate complex abuse patterns and translate findings into automated defenses
Familiarity with common attack patterns: phishing infrastructure, account takeover, credential stuffing, resource abuse
Clear communication skills for working across Security, Support, Legal, and Engineering teams.

Nice to have

Experience at a platform company dealing with user-generated content or compute abuse (hosting providers, cloud platforms, developer tools)
Background in fraud detection, payment abuse, or financial crime
Familiarity with device fingerprinting, IP reputation, and email validation services
Experience with CI/CD security tooling (SAST, SCA, Dependabot, Snyk)
Knowledge of container security, Linux internals, or cloud infrastructure (GCP preferred)
Prior work with abuse reporting pipelines, trust & safety tooling, or content moderation systems

Tools + Tech Stack for this role

Languages: Python, TypeScript, Go, SQL
Data: BigQuery, Hex
Detection tools: Slurper, Netwatch, Stytch (device fingerprint), ClearOut (email reputation)
CI/CD Security: Dependabot, Snyk, SAST/SCA scanners
Infrastructure: GCP, Kubernetes
Collaboration: Linear, Slack, Zendesk (for abuse reports)

This role may not be a fit if

You prefer deep security research over building operational detection systems
You want to focus on vulnerability management, pentesting, or bug bounty triage (that's our Security team)
You're looking for a role with predictable, well-defined problems rather than constantly adapting to adversarial behavior
You prefer working in isolation rather than partnering closely with Support, Legal, and cross-functional teams
You're uncomfortable making enforcement decisions that affect real users

This is a full-time role that can be held from our Foster City, CA office. The role has an in-office requirement of Monday, Wednesday, and Friday.

Full-Time Employee Benefits Include