The AI Behind Fortune 500 Risk: Anton Dam, AuditBoard

From Big Data to Human-Centered AI: Anton on Building Large-Scale Matching and Recommendation Systems

Anton, a veteran machine learning and AI engineer, shares how his work on matching algorithms at Bright.com shaped his perspective on building reliable AI products. Drawing on more than a decade of experience with large-scale recommendation systems and data processing, he explains the foundational role of big data infrastructure in today’s AI landscape.

The discussion breaks down how “embarrassingly parallel” computing enabled massive-scale processing with frameworks like Hadoop and Spark, and how those patterns still influence modern systems. Anton also reflects on how the data scientist role has evolved—from modeling and pipelines to product thinking and cross-functional impact.

A key theme is the shift toward human-centered design: understanding user needs, organizational workflows, and where AI should assist rather than overwhelm. Anton outlines why personalization and pre-filled content can reduce friction while keeping human values in focus.

Key Topics Covered:

• Recruitment matching at Bright.com and the candidate–job scoring problem
• The big data era: Hadoop, Spark, and web 2.0-scale infrastructure
• Embarrassingly parallel computing and practical large-scale data processing
• How recommendation and matching algorithms operate at production scale
• The evolution of the data scientist role over time
• Designing intelligent matching systems that align with real workflows
• Human-centered AI design and the shift from models to user outcomes
• Personalization, pre-filled content, and reducing user effort
• Future-facing considerations: technology choices grounded in human values

Episode Timestamps:

00:00 - Introduction and welcome
00:47 - Anton's experience at Bright.com and the recruitment matching problem
03:23 - The big data era, Hadoop, and Spark in the web 2.0 world
05:00 - Embarrassingly parallel computing and large-scale data processing
10:00 - How recommendation algorithms work at scale in recruitment
20:00 - Evolution of the data scientist role over time
30:00 - Building intelligent matching systems
40:00 - Understanding user needs and organizational workflows
50:00 - The shift toward human-centered AI design
57:30 - Providing pre-filled content and personalization
58:02 - Anton's vision for the future and human values
01:01:46 - Closing thoughts on humanity and technology

About the Guest:

Anton is a machine learning and AI engineer with over a decade of experience building large-scale recommendation and matching systems. He played a key role at Bright.com, a two-sided recruitment platform known for transparent scoring approaches to candidate–job matching. His work spans big data infrastructure, recommendation system design, user experience optimization, and human-centered AI product development.

About the Company:

Bright.com is a two-sided recruitment platform that focused on improving candidate–job matching through scoring algorithms and marketplace-style dynamics. It is referenced in this episode as a formative environment for building and scaling matching systems in a real-world hiring context.