Senior Machine Learning Engineer

About Us

Tyr Ads is a leading tech-driven loyalty and rewards platform that partners with businesses to create meaningful and rewarding experiences for their customers. With millions of users across our platforms, we specialize in innovative AdTech solutions powered by big data, machine learning, and deep learning technologies.

Location:

Bali

Tyr Ads is a leading tech-driven loyalty and rewards platform that partners with businesses to create meaningful and rewarding experiences for their customers. With millions of users across our platforms, we specialize in innovative AdTech solutions powered by big data, machine learning, and deep learning technologies.

Our systems process over 10,000 events per second, delivering real-time insights  build seamless user experiences across web, Android, and iOS, while leveraging ML/AI to power personalization and advertising effectiveness.

We are a team of 90+ employees worldwide and growing, guided by values of transparency, ownership, learning from mistakes, and respect for diverse cultures. Our product development follows Agile methodologies with weekly releases and collaboration tools such as Git Hub, Jira, and Slack.

We're looking for a Senior ML Engineer to build real-time ML systems for our mobile advertising platform. You'll own the full lifecycle - feature engineering, model training, deployment, and production monitoring.

• 5+ years Python, 3+ years ML Engineering
• Strong MLFlow experience (Feature Store, MLflow, Unity Catalog)
• AWS production experience (ECS, S3, Cloud Watch)
• Built real-time ML systems with latency requirements

• AdTech, fraud detection, or recommendation systems experience
• Kafka, Redis, streaming pipelines
• Grafana, Prometheus, Sentry

Tech Stack:
Databricks, PySpark, PyTorch, Light

GBM, FastAPI, AWS ECS, Redis, Kafka, MLflow, Grafana, Prometheus, Sentry

• Real-time, low-latency model serving
• Deep learning (PyTorch) and tree-based models (Light
  
  GBM/XGBoost) behind FastAPI on AWS ECS

• Model promotion workflows: local → staging → production
• Automated validation gates before promotion (offline eval metrics, data quality checks)
• Retraining pipelines via Databricks Workflows (scheduled + drift-triggered)
• CI/CD for both code and model artifacts
• Blue-green or canary deployments for safe rollouts

• Model quality tracking: accuracy, precision, recall, AUC over time
• Data drift and feature drift detection with automated alerts
• Feature freshness monitoring (detecting feature store lag before it impacts predictions)
• Inference latency: p50, p95, p99 via Grafana dashboards
• Sentry for error tracking, Cloud Watch + Prometheus for infra metrics
• A/B testing infrastructure for model experiments in production

• Design low-latency model serving architectures
• Maintain reusable feature pipelines
• Maintain ML models with reliable CI/CD pipelines
• Improve model performance, scalability, and reliability
• Collaborate with data scientists, data engineers, and product teams