SEON · 3 weeks ago
Technical Product Manager, AI Research (Hybrid)
Austin, TX
Full-time
Hybrid
Senior LevelSEON is the command center for fraud prevention and AML compliance, helping thousands of companies worldwide stop fraud and protect revenue. The Technical Product Manager for AI Research will drive a research agenda to innovate document authentication and identity verification systems while ensuring compliance with financial regulations.
Fraud DetectionIdentity ManagementMachine LearningNetwork SecurityRisk Management
Responsibilities
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
Qualification
Required
Deep expertise in computer vision, particularly document understanding, OCR, and information extraction
Strong understanding of modern NLP techniques, especially those applicable to entity recognition, name matching, and contextual understanding
Experience designing and implementing high-assurance AI systems with appropriate verification, validation, and explainability
Demonstrated ability to translate research into working prototypes and production systems
Expertise in LLM behavior engineering for large language models and multimodal AI systems
Demonstrated ability to design and implement effective evaluation frameworks for AI models
Experience with systematically improving AI model performance through iterative testing and refinement
Applied Research experience in Computer Science, Electrical Engineering, or related field
Entrepreneurial mindset with the ability to operate independently and build from first principles
Excellent communication skills with ability to explain complex technical concepts to both technical and non-technical audiences
Preferred
Publication record in relevant research areas is a plus
Experience working on regulated applications of AI, particularly in fintech, identity verification, or compliance
Knowledge of formal verification techniques for AI and machine learning systems
Familiarity with challenges specific to identity documents, including cross-lingual processing, security feature verification, and fraud detection
Track record of building applications that balance performance with interpretability requirements
Experience with hybrid architectures that combine knowledge bases with learned capabilities
Product management experience with AI-powered solutions in enterprise environments
Company
SEON
SEON is the command center for fraud prevention and AML compliance that enriches data, provides context and directs action.
Founded in 2017
201-500 employees
Funding
Current Stage
Growth StageTotal Funding
$187.82MKey Investors
Sixth Street GrowthIVPCreandum
2025-09-16Series C· $80M
2022-04-19Series B· $94M
2021-03-17Series A· $11.98M
Leadership Team
Tamas Kadar
CEO and Cofounder
Angela Pierce
Chief Financial Officer
Recent News
2026-01-19
2026-01-17
2026-01-16
Company data provided by crunchbase