Senior Anti-Fraud Engineer (Full-Time)

Toloka; United States; Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote or Hybrid work model

Competitive compensation

ESOP

Paid PTO

Home office allowances

Job summary

Toloka AI is building fraud-detection systems at the intersection of analytics and applied ML. You will analyze data to uncover patterns, generate hypotheses, and develop defenses—rules, models, and LLM-based approaches—deployed in production.

You’ll own end-to-end delivery from insights to monitoring, and you’ll build data pipelines (SQL, Python, Airflow), design production metrics, and contribute to anti-fraud policy shaping in a remote-friendly hybrid setup.

Qualifications

• Strong SQL skills and ability to query large data sets (ClickHouse a plus).
• Proficiency in Python and ML techniques; experience deploying models to production.
• Experience building data pipelines with Airflow or similar.

Responsibilities

• Explore data to identify fraud patterns, anomalies, and insights.
• Design metrics and build dashboards to visualize protection effectiveness.
• Generate hypotheses from data and rigorously test them.
• Turn hypotheses into production solutions: rules, ML models, and LLM-based approaches.
• Build and maintain data pipelines (SQL, Python, Airflow) to keep defenses up to date.
• Think like an attacker and design defenses to withstand day-one bypass attempts.
• Own area end to end from insight to deployment and monitoring.
• Help shape anti-fraud rules and policies.

Skills

SQL

Python

ML

LLMs

Data pipelines

Airflow

Product sense

Tools

ClickHouse

Databricks

Airflow

Job description

At Toloka AI we create data that powers leading GenAI models and innovations. We work with frontier labs, big tech, renowned AI startups, enterprises and non-profit research organizations worldwide. We use a combination of Experts + Crowd + Tech Platform to teach AI models to reason and evaluate their efficacy and safety. We have experts in more than 50 different domains - from doctors and lawyers to physicists and engineers - and boast one of the most diverse global crowds, representing over 100 countries and speaking 40+ languages. We are a well-funded startup with an enviable portfolio of clients including Anthropic , Amazon , Microsoft , Poolside , Recraft , and Shopify.

Recently, we secured strategic investment led by Bezos Expeditions and Nebius Group with participation from Mikhail Parakhin , CTO of Shopify and board advisor to leading GenAI companies, who now serves as our Chairman of the Board. Our remote-first team is globally distributed around the world: USA , UK , the Netherlands , Serbia , and more.

What this role is about (& Project details)

Our team builds the systems that detect and stop fraud on the Toloka platform. Fraud here is a moving target: bad actors adapt quickly, test our defenses, and increasingly use automation and LLMs to get around them. The same technologies give us a lot of room for new ideas, and we’re looking for someone to help us stay ahead.

You’ll sit at the intersection of analytics and applied ML. You’ll dig into data, find patterns nobody has noticed yet, turn them into hypotheses, and then into working defenses: models and automated pipelines running in production.

What you’ll actually do

• Explore data to find new fraud patterns, anomalies, and insights, and to understand how fraudsters operate.
• Design metrics that show how our protection works, and build dashboards and charts that make them visible.
• Generate hypotheses from data and test them rigorously.
• Turn validated hypotheses into production solutions: rules and heuristics, ML models, and LLM-based approaches.
• Build and maintain data pipelines (SQL, Python, Airflow) that keep our defenses running and up to date.
• Think like an attacker. Anticipate how a new defense could be bypassed, and design it to hold up from day one.
• Own your area end to end: from the first insight to a deployed solution and its ongoing monitoring.
• Help shape anti-fraud rules and policies, including how we define and classify fraud.

What we evaluate

• Analytical thinking. You can frame a vague problem, choose the right metrics, and draw sound conclusions from messy data.
• Hypothesis-driven approach. You generate ideas from data and know how to test them properly.
• SQL. You write confident, efficient queries on large datasets (ClickHouse experience is a plus).
• Python & ML. You can build features, train and evaluate models, and ship them to production. Practical experience with LLMs is a plus.
• Data pipelines. You have working experience with Airflow or similar orchestration tools.
• Product sense. You understand the trade-offs of protection: false positives, impact on honest users, and cost.

Nice to have, none required

• Hands-on experience in anti-fraud, trust & safety, risk, or abuse prevention (a big plus).
• Experience with anomaly detection, graph analysis, or behavioral modeling.
• Experience applying LLMs to analytical or classification tasks.
• Experience with Databricks.
• Experience with crowdsourcing or data annotation platforms.

What we can offer

• You will be part of an international, dynamic environment that drives innovation and sets new standards in the AI and technology sector.
• Competitive compensation package including base salary , bonus , and ESOP.
• Flexibility: we offer remote or hybrid employment. You will also design with your manager a workday that works best for you;
• Paid PTO and benefits will vary depending on location.
• IT setup and home office allowances.
• Personal and career development: we support employee’s ambition for professional development and encourage them to implement their ideas in their projects;