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Yoti operates an identity and age verification platform built on Go, Python, and AWS/GCP infrastructure, with a heavy ML engineering focus reflected in their hiring (7 engineering roles, multiple roles around ML tooling and production model deployment). The project list reveals a company scaling machine learning—deep learning models, ML infrastructure, production pipelines—while pain points cluster around model deployment maturity and system modernization, suggesting they're transitioning from a legacy identity architecture toward AI-powered fraud and deepfake detection.
Tech Stack 29 technologies
Core StackGo Python Linux Salesforce Apex TypeScript JavaScript Node.js Apache Airflow Looker Redshift PostgreSQL HubSpot AWS Git Visualforce Salesforce Lightning Lightning Web Components Google Workspace Microsoft 365 Microsoft Dynamics 365 LookML Bash GCP
What Yoti Is Building
◆Challenges
- Deploying mature ml models into production
- Outdated identity system
- Improving ml system performance
- Maintaining production data pipeline
- Improving scale and efficiency of ml development
- Model deployment challenges
- Scaling machine learning development
▲Active Projects
- Maintain production data pipeline
- Develop ml tooling and infrastructure
- Deploy deep learning models to production
- Machine learning tooling and infrastructure
- Deploy deep learning technology to power yoti products
- Ai-driven legislative scanning tool
- Deploying r&d ml models to production
- Data modeling and semantic layer development
- Bi pipeline optimization
- Data migration support
Hiring Activity
15 roles · 7 in 30d
Department
Seniority
Company intelligence
