Senior Quality Engineer

Domino Data Lab

11d•$145,000 - $175,000•Remote

About The Position

Domino builds software that empowers large, AI-driven organizations to develop and operate advanced data science and AI solutions at scale. Our platform integrates a streamlined model development environment, MLOps capabilities, and features for collaboration, reuse, and reproducibility. This enhances the productivity of data science teams, reduces time to value, and ensures compliance. Our customers, including major organizations in pharmaceuticals, finance, and government, utilize our software for critical applications like developing new medicines, securing financial markets, and national defense. Despite being in business for a decade, Domino maintains a startup spirit, driven by a belief in inventing the future of AI. The Quality Engineering team is central to shipping software with speed and confidence, focusing on embedding smart, scalable practices to improve quality across the engineering organization without hindering delivery speed. This includes developing CI/CD infrastructure and automated testing frameworks that enable rapid and reliable software delivery.

Requirements

Owned a complex automation platform, including end-to-end frameworks, CI systems, and multi-stage automation.
Kubernetes and cloud-native fluency at scale (AWS, Azure, and/or GCP).
Experience shipping compliance-critical software where audit trail, RBAC, or governance correctness affected customer audits (e.g., pharma, finance, govtech, healthtech, MLOps).
Senior-level influence across multiple teams without direct authority.

Nice To Haves

Knowledge of the cucumber library.
Contract testing for integration-heavy products with connector/plugin matrices.
Stateful workflow / approval-flow testing (state machines, gated transitions, policy engines).
Experience with LLM-augmented QA: test generation, triage, or fuzzing with AI.
MLOps or data-platform domain background (model registries, lineage, MLflow, data lake/warehouse).
Scala or Go programming languages.

Responsibilities

Extend the existing automation framework to cover under-covered surfaces and harden it for a higher-velocity merge environment.
Increase automated coverage on critical paths (policy gates, audit trail, RBAC, lineage, data connectors) towards 100% and maintain this level as merge velocity increases.
Drive customer-reported defects on compliance-critical surfaces towards zero.
Build an LLM-assisted test pipeline to match AI-accelerated development with AI-accelerated testing.
Make quality posture visible to leadership through a living risk and coverage map that teams own.