Machine Learning Engineer

About The Company

Join the Future of Commerce with Whatnot! Whatnot is the largest live shopping platform in North America and Europe, revolutionizing the way people buy, sell, and discover their favorite items. We are dedicated to redefining e-commerce by seamlessly blending community engagement, entertainment, and shopping into a vibrant, interactive experience. Our remote co-located team is driven by innovation and grounded in core values that prioritize collaboration and forward-thinking. With operational hubs in the US, UK, Germany, Ireland, and Poland, we are building the future of online marketplaces—creating opportunities for users to connect over a diverse range of products including fashion, beauty, electronics, collectibles like trading cards and comic books, and even live plants. As a rapidly growing marketplace, we are committed to enabling passionate individuals to turn their interests into thriving businesses while fostering a sense of community and shared success.

About The Role

We are seeking a talented and innovative Machine Learning Engineer to join our Trust & Safety team. In this role, you will be responsible for designing, training, and deploying both traditional machine learning models and large language models (LLMs) to detect and prevent fraudulent activities across our platform. Your expertise will help maintain a secure environment by identifying suspicious behaviors related to user accounts, payment transactions, and marketplace interactions. You will lead the development of end-to-end fraud detection systems, balancing platform security with a seamless user experience. Key responsibilities include building behavioral graphs to model user interactions and collusion networks, developing scalable data pipelines supporting high-volume ML workloads, and conducting behavioral and adversarial data analysis to uncover emerging fraud trends. Collaboration with cross-functional teams such as Trust & Safety, Payments, and Infrastructure is essential to develop effective features, labels, and evaluation pipelines. Additionally, you will implement model monitoring systems to ensure ongoing reliability and adapt detection strategies based on evolving fraud tactics. Your contributions will directly impact our ability to automate fraud risk decisions, improve detection accuracy, and enhance overall platform trustworthiness.

Qualifications

Bachelor’s degree in Computer Science, Data Science, or a related field, or equivalent work experience

2–6 years of experience in machine learning, software engineering, or related domains, preferably in risk, fraud, or trust & safety

Strong proficiency in Python and familiarity with ML libraries such as scikit-learn, PyTorch, LightGBM

Solid backend development skills with experience deploying ML models to production environments (batch or real-time)

Experience in data analysis and building data pipelines using SQL, Spark, DBT, or similar tools

Knowledge of fraud detection techniques including chargeback prediction, anomaly detection, and graph-based modeling

Experience with data orchestration frameworks like Dagster or Kubeflow and designing feature stores

Ability to translate business risk scenarios into measurable ML solutions and collaborate effectively across teams

Responsibilities

Design, train, and deploy machine learning models, including LLMs, to identify fraudulent behaviors

Lead the architecture of fraud detection, prevention, and intervention systems to ensure security and user experience balance

Build and maintain behavioral graphs to model user interactions, collusion networks, and account connectivity

Develop scalable data pipelines and real-time inference systems capable of handling high-volume, low-latency workloads

Conduct behavioral and adversarial data analysis to identify emerging fraud trends and improve detection accuracy

Collaborate with Trust & Safety, Payments, and Infrastructure teams to develop features, labels, and evaluation pipelines for models

Implement model monitoring, drift detection, and reliability systems to ensure consistent performance

Contribute to fraud risk orchestration by combining rules, heuristics, and models for automated decision-making

Define key metrics and dashboards to monitor fraud detection effectiveness, including precision, recall, false-positive rate, and latency

Stay informed about emerging fraud tactics and translate insights into adaptive, production-ready solutions

Benefits

Generous holiday and time-off policies to support work-life balance

Comprehensive health insurance options including Medical, Dental, and Vision coverage

Support for remote work with home office setup allowances and monthly stipends for cell phone and internet expenses

Care benefits and wellness allowances to promote overall well-being

Annual childcare allowances and lifetime benefits for family planning, including adoption and fertility expenses

Retirement plans including 401(k) with employer matching in the US and pension plans internationally

Monthly allowances for product testing and app usage to foster deep product understanding

Paid parental leave of 16 weeks plus a gradual return-to-work program

Equal Opportunity

Whatnot is proud to be an Equal Opportunity Employer. We value diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability, or any other protected characteristic. We believe that a diverse workforce enhances our innovation, culture, and overall success, and we encourage individuals from all backgrounds to apply and join our vibrant community.

Seniority level

Associate

Employment type

Full-time

Job function

Information Technology

Industries

Technology, Information and Internet