As a Machine Learning Engineer focused on fraud, you will develop and deploy machine learning models that identify and prevent malicious activity across user accounts, payments, and marketplace interactions. Your work will directly shape the security and integrity of a high-velocity platform, ensuring trust without compromising usability.
What You'll Do
- Design, train, and deploy machine learning models—including large language models—to detect and respond to fraudulent behaviors in real time
- Lead the full lifecycle of fraud detection systems, from architecture and implementation to monitoring and iteration
- Construct behavioral user graphs to uncover patterns of collusion, account networks, and suspicious activity
- Build scalable data pipelines and low-latency inference systems to support real-time decisioning at scale
- Analyze adversarial behavior to identify emerging fraud tactics and improve detection accuracy
- Collaborate with Trust & Safety, Payments, and Infrastructure teams to define features, labels, and evaluation frameworks
- Implement monitoring solutions for model performance, data drift, and system reliability
- Integrate models with rule-based systems and heuristics to power automated risk decisioning
- Define and track key fraud metrics such as precision, recall, false positive rate, and system latency
- Continuously adapt detection strategies to counter evolving fraud techniques
What We're Looking For
- Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience
- 2–6 years of experience in machine learning, software engineering, or risk systems—ideally in fraud, trust & safety, or payments
- Strong programming skills in Python and experience with ML libraries such as scikit-learn, PyTorch, or LightGBM
- Proven ability to deploy ML models into production environments, both batch and real-time
- Experience building data pipelines using SQL, Spark, and DBT
- Familiarity with fraud detection methods including anomaly detection, chargeback prediction, or graph-based analysis
- Hands-on experience with data orchestration tools like Dagster or Kubeflow and feature store design
- Ability to turn business risk challenges into measurable, model-driven solutions
- Strong cross-functional collaboration skills across engineering, data, and product teams
Technology Environment
Python, scikit-learn, PyTorch, LightGBM, SQL, Spark, DBT, Dagster, Kubeflow
Work Environment
This role operates in a hybrid model, requiring proximity to one of our hub locations: San Francisco, New York City, Los Angeles, or Seattle. You’ll have flexibility to work remotely or from an office, with in-person time prioritized for planning, collaboration, and team connection.
Our Culture
We value initiative, curiosity, and action. Team members thrive by focusing on impact, staying close to the product and its users, and prioritizing outcomes over recognition. We move quickly, embrace ambiguity, and learn by doing. Regular use of the platform—as both buyer and seller—is expected to maintain deep user empathy.
Benefits
- Comprehensive health coverage: medical, dental, and vision
- Generous paid time off and holiday policy
- Support for remote work, including home office setup and monthly internet and mobile allowances
- Monthly wellness and app engagement stipends
- Childcare and family planning benefits, including annual childcare support and lifetime coverage for fertility or adoption
- 401k retirement plan with employer match (US) or pension (international)
- 16 weeks of paid parental leave with a structured return-to-work transition
Compensation
For US-based candidates, base salary ranges from $245,000 to $345,000 annually, depending on experience, level, and technical expertise. This does not include equity or benefits. All employees receive stock options as part of their total compensation.
Equal Opportunity
We are committed to equal employment opportunity regardless of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability, or other protected status under local law.
