Technical Product Manager, AI Research (Hybrid)

Technical PM, AI Research

SEON is the leading fraud prevention system of record, catching fraud before it happens at any point across the customer journey. Trusted by over 5,000 global companies, we combine your company's data with our proprietary real-time signals to deliver actionable fraud insights tailored to your business outcomes. We deliver the fastest time to value in the market through a single API call, enabling quick and seamless onboarding and integration. By analyzing billions of transactions, we've prevented $200 billion in fraudulent activities, showcasing why the world's most innovative companies choose SEON.

We are currently looking for a rare combination of research brilliance and pragmatic engineering, someone who can transform cutting-edge computer vision and NLP research into high-assurance AI systems for regulatory compliance and identity verification.

As the founding member of SEON's AI Innovation team, you'll drive a research agenda that reimagines how document authentication, identity verification, and AML screening work in production environments. Your solutions will need to meet the exacting standards of financial regulators while delivering tangible advantages in accuracy, explainability, and cost-effectiveness over legacy systems.

This is an opportunity to build something from first principles. You'll work directly with C-level executives to shape our AI strategy, develop production-ready prototypes, and establish our technical approach to high-stakes decision systems in a regulated environment. Success means creating solutions that can be validated with mathematical rigor, deployed into production environments, and serve as the foundation for SEON's next generation of AI-powered fraud prevention products.

This is a Hybrid role and will be based out of our Austin office.

What You'll Do :

• Design and implement advanced architectures for document understanding that can extract, normalize, and verify complex identity data from diverse document types
• Create novel solutions for AML name screening that incorporate context-aware matching, transliteration handling, and explainable decision frameworks
• Develop evaluation methodologies that rigorously benchmark new approaches against both academic standards and real-world performance metrics
• Design sophisticated AI interaction architectures that optimize model performance across document analysis and fraud prevention systems
• Build systematic frameworks for collecting and analyzing model performance data to guide continuous improvement cycles
• Architect end-to-end systems for document analysis that combine computer vision, OCR, and structured information extraction with robust verification logic
• Build high-assurance systems with appropriate guardrails, confidence scoring, and formal verification methods
• Develop APIs and integration patterns that allow seamless incorporation of AI capabilities into SEON's broader platform
• Create comprehensive evaluation suites for measuring model accuracy, fairness, and reliability against established benchmarks
• Establish methodologies for training, fine-tuning, and continuously improving AI models with domain-specific data
• Evaluate emerging research, tools, and platforms from major cloud providers and research institutions
• Develop product-focused neural reasoning frameworks that balance performance with explainability requirements
• Create innovative evaluation methodologies that validate AI systems against both technical and business metrics
• Partner with product management to translate research innovations into market-ready capabilities
• Collaborate with compliance and legal teams to ensure AI systems meet regulatory requirements for explainability and audibility
• Build relationships with academic researchers and industry partners to accelerate innovation
• Mentor and guide engineers as the Austin AI Innovation team grows

What You'll Bring :

(Amazing If You Also Have) :