Staff Software Engineer, Risk

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering Verified - from the job posting source checked Jun 20, 2026

Seniority

Staff Plus Verified - from the job posting source checked Jun 20, 2026

Work mode

Hybrid Inferred - source not recordedchecked Jun 20, 2026

In-office days

2 days Inferred - source not recordedchecked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Staff Software Engineer, Risk Foster City, CA | Hybrid | Remote 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 Risk 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

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