About the Role: We’re building a large-scale synthetic data generation engine to produce realistic observability datasets — metrics, logs, and traces — to support AI/ML training and benchmarking. You will design, implement, and scale pipelines that simulate complex production environments and emit controllable, parame
About the role
About the Role:
We’re building a large-scale synthetic data generation engine to produce realistic observability datasets — metrics, logs, and traces — to support AI/ML training and benchmarking. You will design, implement, and scale pipelines that simulate complex production environments and emit controllable, parameterized telemetry data.
🧠 What You’ll Do
• Design and implement generators for metrics (CPU, latency, throughput) and logs (structured/unstructured).
• Build configurable pipelines to control data rate, shape, and anomaly injection.
• Develop reproducible workload simulations and system behaviors (microservices, failures, recoveries).
• Integrate synthetic data storage with Prometheus, ClickHouse, or Elasticsearch.
• Collaborate with ML researchers to evaluate realism and coverage of generated datasets.
• Optimize for scale and reproducibility using Docker containers.
✅ Who You Are
• Strong programming skills in Python.
• Familiarity with observability tools (Grafana, Prometheus, ELK, OpenTelemetry).
• Solid understanding of distributed systems metrics and log structures.
• Experience building data pipelines or synthetic data generators.
• (Bonus) Knowledge of anomaly detection, time-series analysis, or generative ML models.
💸 Pay
$50 - 75/hr depending on experience
Remote, flexible hours
Project timeline: 5-6 weeks
Official source: expert-hub.sepalai.com. Details can change; the platform's page is the final word. How we check listings.
