Key Skills

Job Description

Company Intro 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 from Netherlands, with a flexible work schedule that you can design together with your manager to best suit your needs. 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; To learn how we collect, use, disclose, and store personal data, check out our Privacy Notice.

Core Responsibilities

Analyze platform data to identify fraud patterns, develop and test hypotheses, and create metrics and dashboards to monitor protection effectiveness. Build and maintain production fraud defenses—including rules, machine-learning models, LLM-based approaches, and data pipelines—and own their ongoing monitoring and policy development.

Requirements

The role requires strong analytical and hypothesis-testing skills, confident SQL for large datasets, and practical Python and machine-learning experience, including deploying models to production. Experience with Airflow or similar orchestration tools and sound judgment about false positives and user impact are expected; anti-fraud, LLM, anomaly detection, graph analysis, Databricks, and crowdsourcing experience are advantageous.

Benefits

• Bonus
• Employee Stock Ownership Plan
• Flexible Work Schedule
• Paid Time Off
• IT Setup Allowance
• Home Office Allowance
• Professional Development Support