Runware is building a high-performance AI media-creation platform powering instant generation of text, image, video, 3D, and audio. As our platform scales and integrations grow, we need robust, reliable, and high-throughput data systems.
We’re looking for a
Data Engineer
to architect and maintain our data backbone — with a special focus on high-volume logs, and performance pipelines.
You will work hand-in-hand with our Data Expert and the platform team to transform raw system activity into structured, queryable, high-value data.
🎯 Mission
Your main mission is to
build, optimize, and maintain Runware’s data infrastructure
.
You will ensure that logs, metrics, performance data, and events are efficiently ingested, processed, stored, and ready to be analyzed by engineering, ML, and product teams
- This role is central to: - Supporting observability & platform reliability
- Enabling deep log & performance analytics
- Powering internal dashboards and customer reporting
- Providing clean, structured data to the Data Expert and all stakeholders
🧩 What You Will Do
Architecture & Ownership
- Design, build, and maintain
schemas and data models
- Optimize table layout, partitioning, indexing, and compression for high-volume data
- Ensure fast, efficient querying for logs, requests, metrics, and performance traces
- Maintain ingestion pipelines for billions of records
Data Engineering & Pipelines
- Build robust pipelines for:
- API logs
- Model inference logs
- Error events
- Usage & integration events
- GPU & system metrics
- Implement ETL/ELT workflows to transform raw data into analytics-ready structures
- Ensure quality, reliability, and real-time availability of data sources
Performance & Log Analysis Infrastructure
- Build tooling to support large-scale
log analysis
- Enable deep investigation into latency, throughput, errors, and bottlenecks
- Provide the raw data foundation for E2E inference-time monitoring
- Help debug production issues using logs and traces
Tooling & Observability Infrastructure
- Work closely with DevOps, ML, and backend engineering
- Integrate pipelines with monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry)
- Automate ingestion and cleanup tasks
- Build internal libraries or utilities to support monitoring and debugging workflows
Collaboration & Cross-Functional Support
- Provide clean data interfaces for the Data Expert (dashboards, monitoring, analytics)
- Support engineering teams by exposing the right logs and metrics
- Contribute to debugging, RCA (root cause analysis), and performance optimization initiatives
Requirements
What We’re Looking For
Data Engineer
or similar role in a production environment
data pipelines
, streaming vs batch processing, and data modeling
analytical databases
(ClickHouse is a plus, but not mandatory)
- Comfortable digging through
logs, metrics, and platform data
to understand system behavior
event-based systems, monitoring, and observability concepts
- Pragmatic mindset: you care about usefulness, reliability, and performance over theory
- Comfortable working cross-functionally with backend, infra, and data profiles
- Startup / scale-up experience is a plus
Nice to Have
- Experience with high-throughput or realtime systems
- Exposure to cost monitoring, performance analytics, or platform observability
- Background in AI, ML platforms, or data-heavy products
Benefits
We’re a remote-first collective, meeting in person twice a year to plan, brainstorm, celebrate wins, and enjoy some face-to-face time. We have core hours for cooperative working and calls, but outside of that your calendar is yours. Work the hours that let you perform at your peak while also building a healthy life.
Our release cycles are fast and intense, but they’re followed by real downtime. After big pushes we expect the team to unplug, recharge, and come back ready & stronger than ever for the next leap.
Generous paid time off
– vacation, sick days, public holidays
Meaningful stock options
– share in the upside you create
Remote-first setup
– work from home anywhere we can employ you
Flexible hours
– own your schedule outside core collaboration blocks
Family leave
– paid maternity, paternity, and caregiver time
Company retreats
– twice-yearly gatherings in inspiring locations
