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
Education: Bachelor's degree completed
Official source: expert-hub.sepalai.com. Details can change; the platform's page is the final word. Something wrong with this listing? Tell us.
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