You will develop and iterate on account signals and prevention frameworks that consolidate internal and external data into actionable abuse indicators.

You will develop and optimize identity and account-linking signals using graph-based data infrastructure to detect coordinated and scaled account abuse.

You will evaluate, integrate, and operationalize third-party vendor signals , assessing whether new data sources provide genuine lift in detection.

You will expand internal account signals with new data sources and behavioural indicators to improve detection coverage.

You will build and maintain processes that evaluate new product launches for scaled abuse risks, working closely with product teams to ensure enforcement readiness.

You will operationalize and iterate on enforcement tooling , including appeals workflows, review processes, and user communications , to maintain quality and scale with growing volume.

You will analyse enforcement performance through operational metrics, partnering with the team to keep detection accurate as abuse patterns evolve.

You will manage payment fraud and dispute operations to protect revenue and maintain our standing with payment partners.

You will coordinate enforcement efforts for policy compliance gaps across products, working with relevant teams to build scalable review processes.

You will collaborate with cross-functional teams (Engineering, Product, Legal, Data Science) to surface new signals and translate detection capabilities into enforcement workflows.

You will maintain detailed documentation of signal development, enforcement processes, and operational decisions.

This role requires 2+ years of experience in risk scoring, fraud detection, trust and safety, or policy enforcement.

You should have hands-on experience building detection systems, risk models, or enforcement processes and workflows.

You should have experience evaluating and integrating third-party data sources into detection or scoring pipelines.

You should have strong SQL and Python skills , this role involves heavy data analysis across complex, multi-table data relationships.

You should have familiarity with identity signals such as device fingerprinting, account linking, or entity resolution, or experience with appeals processes and customer-facing enforcement communications.

You should have demonstrated ability to analyse complex data problems and translate findings into actionable improvements.

You should have strong written and verbal communication skills , ability to explain technical tradeoffs and navigate cross-functional stakeholder conversations.

Equivalent practical experience or a Bachelor's degree in Computer Science, Data Science, or related field is required.

This role offers an annual salary range of $230,000-$310,000 USD.

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Your work will directly shape what users experience: designing APIs that serve rich content, building services that handle real-time interactions, implementing content-matching systems for rights and safety, and ensuring our platform performs under load. You'll architect systems that are fast, correct, and maintainable.

You'll collaborate closely with Product, ML Research, and Mobile/Web teams to ship features that matter. We use Python, Go, BigQuery, Pub/Sub, and a microservices architecture,but we care more about good judgment than specific tool experience.

Responsibilities

• Design and maintain application-level data models that organize rich content into canonical structures optimized for product features, search, and retrieval.
• Build high-reliability ETLs and streaming pipelines to process usage events, analytics data, behavioral signals, and application logs.
• Develop data services that expose unified content to the application, such as metadata access APIs, indexing workflows, and retrieval-ready representations.
• Implement and refine fingerprinting pipelines used for deduplication, rights attribution, safety checks, and provenance validation.
• Own data consistency between ingestion systems, application surfaces, metadata storage, and downstream reporting environments.
• Define and track key operational metrics, including latency, completeness, accuracy, and event health.
• Collaborate with Product teams to ensure content structures and APIs support evolving features and high-quality user experiences.
• Partner with Analytics and Research teams to deliver clean usage datasets for experimentation, model evaluation, reporting, and internal insights.
• Operate large analytical workloads in BigQuery and build reusable Dataflow/Beam components for structured processing.
• Improve reliability and scale by designing robust schema evolution strategies, idempotent pipelines, and well-instrumented operational flows.

Requirements

• Experience building production backend services and APIs at scale
• Experience building ETL/ELT pipelines, event processing systems, and structured data models for applications or analytics
• Strong background in data modeling, metadata systems, indexing, or building canonical representations for heterogeneous content
• Proficiency in Python, Go, SQL, and scalable data-processing frameworks (Dataflow/Beam, Spark, or similar)
• Familiarity with BigQuery or other analytical data warehouses and strong comfort optimizing large queries and schemas
• Experience with event-driven architectures, Pub/Sub, or Kafka-like systems
• Strong understanding of data quality, schema evolution, lineage, and operational reliability
• Ability to design pipelines that balance cost, latency, correctness, and scale
• Clear communication skills and an ability to collaborate closely with Product, Research, and Analytics stakeholders

Nice to Have

• Experience building application-facing APIs or microservices that expose structured content
• Background in information retrieval, indexing systems, or search infrastructure
• Experience with fingerprinting, perceptual hashing, audio similarity metrics, or content-matching algorithms
• Familiarity with ML workflows and how downstream analytics and usage data feed back into research pipelines
• Understanding of batch + streaming architectures and how to blend them effectively
• Experience with Go, Next.js, or React Native for occasional full-stack contributions

Why Join Us

You will design the core data services and pipelines that power our product experience, analytics, and business operations. You’ll work on high-impact data challenges involving real-time signals, large-scale metadata systems, and cross-platform consistency. You’ll join a small, fast-moving team where you’ll shape the structure, reliability, and intelligence of our downstream data ecosystem.

Benefits

• Highly competitive salary and equity
• Quarterly productivity budget
• Flexible time off
• Fantastic office location in Manhattan
• Productivity package, including ChatGPT Plus, Claude Code, and Copilot
• Top-notch private health, dental, and vision insurance for you and your dependents
• 401(k) plan options with employer matching
• Concierge medical/primary care through One Medical and Rightway
• Mental health support from Spring Health
• Personalized life insurance, travel assistance, and many other perks

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Anthropic is an AI safety and research company working to build reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our customers and society as a whole. As a Safeguards Analyst focusing on Account Abuse, you will play a critical role in building and scaling the detection, enforcement, and operational capabilities that protect our platform against scaled abuse.

Responsibilities:

• Develop and iterate on account signals and prevention frameworks that consolidate internal and external data into actionable abuse indicators
• Develop and optimize identity and account-linking signals using graph-based data infrastructure to detect coordinated and scaled account abuse
• Evaluate, integrate, and operationalize third-party vendor signals — assessing whether new data sources provide genuine lift in detection
• Expand internal account signals with new data sources and behavioural indicators to improve detection coverage
• Build and maintain processes that evaluate new product launches for scaled abuse risks, working closely with product teams to ensure enforcement readiness
• Operationalize and iterate on enforcement tooling — including appeals workflows, review processes, and user communications — to maintain quality and scale with growing volume
• Analyze enforcement performance through operational metrics, partnering with the team to keep detection accurate as abuse patterns evolve
• Manage payment fraud and dispute operations to protect revenue and maintain our standing with payment partners
• Coordinate enforcement efforts for policy compliance gaps across products, working with relevant teams to build scalable review processes
• Collaborate with cross-functional teams (Engineering, Product, Legal, Data Science) to surface new signals and translate detection capabilities into enforcement workflows
• Maintain detailed documentation of signal development, enforcement processes, and operational decisions

Qualifications:

• 2+ years of experience in risk scoring, fraud detection, trust and safety, or policy enforcement
• Hands-on experience building detection systems, risk models, or enforcement processes and workflows
• Experience evaluating and integrating third-party data sources into detection or scoring pipelines
• Strong SQL and Python skills — this role involves heavy data analysis across complex, multi-table data relationships
• Familiarity with identity signals such as device fingerprinting, account linking, or entity resolution, or experience with appeals processes and customer-facing enforcement communications
• Demonstrated ability to analyze complex data problems and translate findings into actionable improvements
• Strong written and verbal communication skills — ability to explain technical tradeoffs and navigate cross-functional stakeholder conversations
• Equivalent practical experience or a Bachelor's degree in Computer Science, Data Science, or related field

You might be a good fit if you:

• Have built risk scores, detection systems, signal pipelines, or enforcement processes in a previous role — identity verification, trust and safety, or similar
• Are comfortable working with ambiguous, noisy data and extracting meaningful signal
• Think critically about signal quality and enforcement performance — evaluating whether new detection signals or processes meaningfully improve outcomes
• Have experience with graph-based data, account-linking problems, or cross-functional process design
• Are proactive about identifying gaps in existing detection or enforcement and proposing new approaches
• Have experience leveraging generative AI tools to support analytical, detection, or enforcement workflows
• Can balance deep analytical work with cross-functional collaboration and stakeholder coordination
• Have a background or interest in cybersecurity or threat intelligence (a plus, not a requirement)

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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Start with an opening paragraph (no heading): what the role is, who the company is, why it matters. If the ad mentions salary, include it here.

Rephrase bullet points in your own words while keeping the factual content. Combine related points where it makes sense.

For benefits/perks: gather them from anywhere in the ad into one section. If the ad mentions nothing about benefits, omit a benefits section entirely.

Do not invent information that is not in the original ad.

About the role The Anti-Abuse 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 ⚕️ Health, Dental, Vision and Life Insurance 🩼 Short Term and Long Term Disability 🚼 Paid Parental, Medical, Caregiver Leave 🚗 Commuter Benefits 📱 Monthly Wellness Stipend 🧑‍💻 Autonomous Work Environment 🖥 In Office Set-Up Reimbursement 🏝 Flexible Time Off (FTO) + Holidays 🚀 Quarterly Team Gatherings ☕ In Office Amenities

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