About Parlay Finance
AI-native intelligence for finance. Parlay modernizes the credit ecosystem with enterprise-grade technology that turns fragmented data into compounding value. Backed by Techstars.
About the role
ARRISE is a global leader in software development and services for the iGaming industry, bringing together more than 2,500 professionals operating across more than 12 locations worldwide. From concept through to delivery, we combine technical expertise, creativity and industry knowledge to develop products, platforms and services that shape player experiences and support some of the industry's leading brands. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- Strong understanding of deep learning–based recommender systems for next-item prediction, and analogous NLP architectures that model sequential patterns and context
- Experience with unit and integration testing (e.g., Pytest), CI/CD pipelines, and Docker-based containerization
More about this role
- Design, implement, and optimize end-to-end recommendation pipelines, from data ingestion to model inference.
- Build and maintain scalable ETL pipelines to support reliable and efficient data flows.
- Develop, evaluate, and continuously improve ML models for recommendation systems.
- Research, prototype, and implement state-of-the-art (SOTA) approaches to improve recommendation quality and drive key business metrics.
- Scale and optimize data and model pipelines to handle large volumes of data and real-time or batch processing needs.
- Integrate multi-modal data (e.g., behavioral, transactional, and contextual signals) from various systems into recommendation models.
- Ensure robustness and stability of pipelines by implementing unit and integration tests across data, modeling, and deployment workflows.
- Monitor and maintain end-to-end system performance, including data pipelines, model quality, and downstream impact.
- Design and analyze A/B tests to evaluate model performance and support data-driven product decisions.
- Build dashboards and observability tools to track model metrics, system health, and business KPIs.
- Collaborate closely with Data Engineers, Software...
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