Machine Learning Engineer for Fraud Detection

4 days ago

Sydney, New South Wales, Australia beBeeFraud Full time $180,000 - $250,000

Job Overview

We're seeking an accomplished machine learning engineer to develop cutting-edge AI systems for fraud detection and prevention. You'll leverage the latest advancements in graph ML, transformers, and behavioral analytics to combat sophisticated threats.

  • Fraud Detection: Identify fake profiles, compromised credentials, and anomalous login behavior.
  • Abuse Prevention: Develop scalable systems to detect communities of malicious actors and their on-chain patterns.
  • Toll Fraud & Drainers: Detect gas draining contracts, malicious token airdrops, and phishing-induced approval scams.
  • Money Laundering: Identify cross-chain obfuscation, mixer usage, layering, and integration strategies.

You'll collaborate with cross-functional teams to design, build, and deploy end-to-end machine learning systems that detect, explain, and prevent these behaviors in real time.

Key Responsibilities

  • Design and implement AI models using transformer architectures, GNNs, and unsupervised learning for anomaly and abuse detection.
  • BUILD high-performance fraud scoring systems that can operate in real-time at massive scale.
  • Collaborate with campaign & marketing teams to identify abuse vectors in rewards, referral, and incentive systems.
  • Conduct regular performance evaluations of ML models and continuously improve them using new research and adversarial learning techniques.
  • Support fraud investigation workflows by providing interpretable model results and actionable insights.

Qualifications

Must-Have:

  • Bachelor's or Master's degree in Computer Science, Data Science, or related field.
  • 5+ years of hands-on experience in machine learning and fraud detection, ideally in fintech, e-commerce, or Web3.
  • Strong background in:
  • Graph machine learning, GNNs, and clustering algorithms
  • Transformer models and attention mechanisms
  • Fraud detection techniques, including anomaly detection and behavioral scoring
  • Feature engineering from high-dimensional, noisy, and heterogeneous data sources
  • Proficiency in Python and ML frameworks like PyTorch/TensorFlow.
  • Familiarity with big data processing tools (Spark, Hive) and real-time data pipelines (Kafka, Flink).
  • Solid understanding of EVM-compatible blockchain data (transactions, contract calls, logs) and wallet behaviors.

What We Offer

  • Competitive salary and benefits, plus equity opportunities for senior candidates.
  • Opportunity to work on cutting-edge risk systems that secure one of the world's most active trading ecosystems.
  • A collaborative, fast-paced environment that values ownership, speed, and innovation.
  • Access to a global team of experts across fraud, security, AI, and blockchain.

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