We’re building the world’s first Creator Casino: a creator-powered gaming network where influencers launch Rooms, run drops/missions, and monetize Originals built on top of certified game engines—powered by an AI Host layer and programmable incentives designed for high-frequency sessions and measurable creator performance.
This role helps us scale creator-led distribution, room economics, and session-velocity gameplay—without compromising trust, compliance, or margin.
Role Overview
We’re seeking an ML Engineer to build applied models for personalization and risk signals—production-grade, measurable, and designed for real-world iteration.
Core Tools & Systems (where this role operates)
Claude (Anthropic API + Console) (prompting tools, templates/variables, evals, tool-use patterns)
AWS Bedrock (genAI applications/agents, knowledge bases, guardrails)
AWS SageMaker (training/deployment + MLOps lifecycle)
SageMaker Model Monitor (production monitoring + drift detection)
LangChain / LangGraph (agent orchestration and tool integrations)
LlamaIndex (RAG / data framework for context-augmented LLM apps)
W&B or MLflow (experiment tracking, model registry, reproducibility)
Apache Airflow (workflow orchestration for ML/data jobs)
OpenTelemetry (OTel) (tracing/metrics/logs instrumentation for AI services)
ClawBot (internal agent workflows and automation support)
Responsibilities
Model personalization by creator affinity and room participation (recommendations, next-best incentive, next-best room)
Build incentive optimization and uplift models (missions/drops/power-ups, VIP uplift, retention lift by cohort)
Predict session frequency and habit loops; measure and improve repeat play through experimentation and modelingBuild and productionize models for retention and personalization.
Use lifecycle outcomes as measurable training signals.
Incorporate abuse/leakage signals where relevant for anomaly and risk features.
Define evaluation loops, monitoring, and internal AI agent/automation workflows so production systems remain stable and scalable over time.
AI Agents / Internal Automation (agent workflows for simulations, regression checks, and report generation)
Required Skills & Experience
4+ years applied ML with production deployment experience, ideally in crypto casino / crypto gaming or adjacent high-frequency consumer products.
Strong Python + practical MLOps fundamentals.
Strong understanding of experimentation and measurement to validate real impact.
Comfort collaborating with Product and Engineering on production constraints.
Nice to Have
Experience in high-frequency consumer products (games, social, marketplaces) where behavior loops matter
Comfortable designing models that directly shape product incentives and player journeys
Experience with recommender systems and lifecycle personalization.
Experience with anomaly detection and risk feature engineering.
Experience with causal inference or uplift modeling for retention interventions.
Experience building monitoring for drift and performance degradation.