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Adversarial Attack Detection Jobs (NOW HIRING)

This is adversarial work: attackers adapt constantly, and we build the detection systems ... Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection ...

This is adversarial work: attackers adapt constantly, and we build the detection systems ... Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection ...

This is adversarial work: attackers adapt constantly, and we build the detection systems ... Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection ...

This is adversarial work: attackers adapt constantly, and we build the detection systems ... Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection ...

This is adversarial work: attackers adapt constantly, and we build the detection systems ... Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection ...

This is adversarial work: attackers adapt constantly, and we build the detection systems ... Analyze attack patterns using BigQuery and Hex, turning investigation findings into new detection ...

The selected candidate will author custom detection analytics and hunt tooling, execute proactive ... identify adversarial artifacts, anomalies, Indicators of Attack (IOAs), and Indicators of ...

Cyber Software Engineer

Augusta, GA · On-site

$104K - $166K/yr

The selected candidate will author custom detection analytics and hunt tooling, execute proactive ... identify adversarial artifacts, anomalies, Indicators of Attack (IOAs), and Indicators of ...

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Adversarial Attack Detection information

What is adversarial attack detection?

Adversarial attack detection refers to the process of identifying and mitigating attempts to fool machine learning models by introducing subtle, intentionally crafted inputs known as adversarial examples. These attacks can cause models, especially in image, speech, and text recognition, to make incorrect predictions or classifications without obvious changes to human observers. Detecting such attacks is crucial for ensuring the robustness and security of AI systems, particularly in sensitive applications like autonomous vehicles, healthcare, and cybersecurity. Methods include statistical analysis, anomaly detection, and model training techniques specifically designed to spot adversarial manipulations.

What are the key skills and qualifications needed to thrive as an Adversarial Attack Detection Specialist, and why are they important?

To thrive in Adversarial Attack Detection, you need a strong background in computer science, machine learning, and cybersecurity, often supported by a relevant degree and experience in AI security. Familiarity with frameworks like TensorFlow or PyTorch, knowledge of adversarial machine learning techniques, and certifications such as Certified Ethical Hacker (CEH) are typically required. Analytical thinking, problem-solving, and effective communication skills help professionals identify threats and collaborate with cross-functional teams. These skills and qualities are crucial for detecting, mitigating, and preventing sophisticated attacks on AI systems, thereby ensuring the integrity and reliability of digital infrastructure.

What is the difference between Adversarial Attack Detection vs Cybersecurity Analyst?

AspectAdversarial Attack DetectionCybersecurity Analyst
Required CredentialsKnowledge of machine learning, AI security, certifications like CEH or CISSP beneficialCertifications like CISSP, CISA, or Security+ often required
Work EnvironmentFocus on AI systems, machine learning models, and threat detection toolsNetwork security, incident response, and vulnerability assessment in IT environments
Industry UsagePrimarily in AI, tech, and cybersecurity sectors dealing with AI model securityAcross finance, government, healthcare, and tech sectors for overall security

Adversarial Attack Detection specialists focus on identifying and mitigating threats aimed at AI models, while Cybersecurity Analysts handle broader security threats across IT systems. Both roles require security certifications but differ in their technical focus and work environment.

What are some common challenges faced when working in adversarial attack detection roles?

Professionals in adversarial attack detection often face the challenge of keeping up with rapidly evolving attack techniques, as adversaries continually develop new methods to bypass defenses. Another common difficulty is balancing security measures with system usability, ensuring that detection does not introduce excessive false positives or degrade user experience. Additionally, collaboration with other cybersecurity and machine learning teams is crucial, as sharing insights and data can improve detection accuracy and response times.
Infographic showing various Adversarial Attack Detection job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 90% Full Time, 6% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution.
Senior Software Engineer, Fraud

Senior Software Engineer, Fraud

Replit

San Mateo, CA

$139K - $183K/yr

Other

Medical, Dental, Vision, Life, Retirement

Posted 3 days ago


Job description

Fraud Detection Engineer

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.

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:

  • Competitive Salary & Equity
  • 401(k) Program with a 4% match (US Only)
  • Health, Dental, Vision and Life Insurance
  • Short Term and Long Term Disability
  • Paid Parental, Medical, Caregiver Leave
  • Flexible Time Off (FTO) + Holidays
  • Commuter Benefits (In-Office Only)
  • Monthly Wellness Stipend
  • Autonomous Work Environment
  • In Office Set-Up Reimbursement (In-Office Only)
  • Quarterly Team Gatherings
  • In Office Amenities (In-Office Only)

To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.