Partner with Data Science, ML Engineering, Product, and Customer Engineering teams to develop, evaluate, deploy, and continuously improve fraud detection capabilities for customer use cases.
Identify, prioritize, and integrate fraud signals across transactional, identity, account, device, network, application, behavioral, and operational data.
Establish frameworks for rapidly translating newly discovered fraud patterns into production rules, signals, models, and detection workflows that can be adopted across customer environments.
Define and monitor detection effectiveness using metrics such as precision, recall, false-positive rates, detection coverage, alert quality, latency, and business impact.
Lead technical root-cause analysis when fraudulent activity bypasses existing controls and drive improvements to detection logic, data coverage, and system resilience.
Build reusable detection infrastructure, services, APIs, reference architectures, and frameworks that support multiple products, fraud types, customer environments, and business use cases.
Evaluate emerging technologies and determine where AI, machine learning, graph analytics, automation, and analyst-assist tooling can improve fraud detection and response.
Lead technical build and architecture reviews, driving alignment across Development, Data Science, Security, Risk, Product, Customer Engineering, and Service Delivery collaborators.
What We Need to See
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent experience. 15+ years of progressive experience in software engineering or a related technical discipline, including 6+ years of experience leading and managing complex, cross-functional engineering organizations and delivering high-impact technical products or platforms. Deep expertise in one or more of the following areas is required: software engineering, fraud technology, risk systems, security engineering, machine learning, data science, or data engineering.
Significant experience designing, building, or operating large-scale fraud, abuse, risk, security, detection, or machine learning systems.
Experience developing platforms, products, APIs, services, or technical frameworks that are adopted by internal or external customers to build production systems.
Strong understanding of detection methodologies, including rules-based, statistical, behavioral, anomaly-based, graph-based, and machine-learning approaches.
Experience designing real-time, high-volume, distributed, or event-driven data processing systems.
Experience with data pipelines, feature engineering, model inference, production ML systems, or decisioning platforms.
Demonstrated ability to identify meaningful signals within large and complex datasets and translate them into actionable detection capabilities.
Experience defining metrics and using data to evaluate and improve detection-system effectiveness.
Strong systems-thinking skills and the ability to turn ambiguous fraud, abuse, customer, or threat patterns into clear technical requirements and scalable solutions.
Demonstrated experience leading complex technical initiatives across multiple engineering, data, risk, product, and customer-facing teams.
Ways to Stand Out from the crowd
Deep experience with machine learning-based fraud detection, anomaly detection, behavioral modeling, entity risk scoring, or adaptive risk systems.
Experience with graph analytics, graph machine learning, entity resolution, link analysis, or identifying coordinated activity across complex networks of entities and developing systems that detect adaptive or adversarial behavior where attack patterns change in response to existing controls.
Experience applying generative AI or LLMs to fraud detection, investigations, threat analysis, case summarization, or analyst workflows and designing fraud technology platforms that support multiple products, organizations, geographies, customer environments, or fraud use cases rather than individual point solutions.
Experience designing low-latency inference, streaming, event-processing, or real-time decisioning systems.
Experience developing automated feedback loops that use confirmed fraud, investigation outcomes, customer disputes, chargebacks, or analyst decisions to improve models and detection logic.
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing, and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction — from artificial intelligence to autonomous vehicles. NVIDIA is widely considered one of the technology world's most desirable employers. We have some of the most forward-thinking and hard-working people in the world working for us. If you're passionate about building the infrastructure that runs AI at scale, we want to hear from you.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 320,000 USD – 488,750 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until November 3, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
