Senior Software Engineer, Fraud

About the role

The Fraud Experience team at Whatnot builds intelligent, real-time systems to safeguard the marketplace from malicious activity. This role involves leading the architecture of fraud detection, prevention, and intervention systems, designing and deploying ML models, and analyzing data to improve systems.

Responsibilities

• Lead the full architecture of fraud detection, prevention, and intervention systems — spanning machine learning, backend, and client-side components.
• Build intelligent user graphs to model behavioral patterns, detect collusion networks, and uncover account connectivity at scale.
• Design, train, and deploy both traditional ML and LLM-powered models to detect fraudulent activity across users, payments, and marketplace interactions.
• Create scalable data pipelines and real-time inference systems capable of supporting high-volume, low-latency ML workloads.
• Develop human-in-the-loop systems that continuously refine detection accuracy and adapt to evolving adversarial tactics.
• Perform deep behavioral and adversarial data analysis to surface emerging fraud trends and drive continuous system improvement.

Requirements

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Economics, or a related technical field.
• 4+ years of software engineering experience building systems for consumer-scale traffic and reliability.
• 1+ years of writing production-grade Python code and working with ML libraries (e.g. PyTorch, LightGBM).
• 1+ years of experience in machine learning or fraud prevention domains.
• Deep business intuition and a data-driven mindset — you think critically about how abuse prevention systems affect growth and user experience.
• Fluency with data tooling, including data warehouses (e.g. Snowflake) and transformation frameworks (e.g. dbt, Dagster).
• Strong communication skills and the ability to lead initiatives across product areas, collaborating closely with leadership, data science, and product teams.
• Experience working in a remote-first environment and producing well-tested, reproducible work.

Qualifications

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Economics, or a related technical field.
• 4+ years of software engineering experience building systems for consumer-scale traffic and reliability.
• 1+ years of writing production-grade Python code and working with ML libraries (e.g. PyTorch, LightGBM).
• 1+ years of experience in machine learning or fraud prevention domains.
• Deep business intuition and a data-driven mindset — you think critically about how abuse prevention systems affect growth and user experience.
• Fluency with data tooling, including data warehouses (e.g. Snowflake) and transformation frameworks (e.g. dbt, Dagster).
• Strong communication skills and the ability to lead initiatives across product areas, collaborating closely with leadership, data science, and product teams.
• Experience working in a remote-first environment and producing well-tested, reproducible work.

Skills

• Hands-on machine learning or data science experience in production environments.

Benefits

• Generous Holiday and Time off Policy
• Health Insurance options including Medical, Dental, Vision
• Work From Home Support
• Home office setup allowance
• Monthly allowance for cell phone and internet
• Care benefits
• Monthly allowance to dogfood the app
• All Whatnauts are expected to develop a deep understanding of our product.
• Compensation Range: $200K - $230K