Domino serves over 20% of Fortune 100 enterprises with an MLOps platform built on Python, Kubernetes, Kafka, and Spark—a stack shaped for distributed model training and production inference. The product roadmap signals a pivot toward governance (active governance feature integration and AI project control) and LLM hosting, while adopting Codex and Cursor suggests internal shift toward AI-assisted development. Hiring is engineering-heavy (8 of 14 open roles) with seniority skewed toward senior and staff-level, indicating either scale-out of core platform capabilities or leadership gaps in emerging areas like governance UX.
Tech Stack 26 technologies
Core StackPython Go TypeScript React GraphQL Kubernetes Docker PostgreSQL Redis Kafka AWS Apache Spark gRPC Hadoop Java Scala Prometheus New Relic CircleCI MLflow Domino Azure GCP Ray
What Domino Data Lab Is Building
◆Challenges
- Cloud adoption
- Reducing incident recurrence
- New challenges for deploying ai systems
- Govern ai projects effectively
- Improve governance feature user experience
- Resource misconfigurations
- Regressions in system behavior
- Slow ai project time to production
- Measuring content performance
- Model development for drug discovery
▲Active Projects
- Model development lifecycle platform
- Custom solutions integration with domino
- Model monitoring integration
- Automate cs initiatives
- Cloud adoption
- Governance feature set integration
- Automated model documentation
- Next generation mlops capabilities
- Llm hosting capabilities expansion
- Tempest platform development
Hiring Activity
15 roles · 9 in 30d
Department
Seniority
Company intelligence