Head of QA
This isn’t another chatbot or automation layer.
This platform is using voice AI real-world clinical data to power empathetic, human-like conversations between healthcare systems and patients - at scale. Think:
- Real-time voice interactions
- Emotional signal detection (not just what is said, but how )
- AI-driven care workflows across health plans, providers, and pharma
Now they need someone to own quality across the entire system . Why This Role is Interesting This is not a traditional QA leadership role.
You’re not just testing features.
You’re responsible for quality across a full AI data real-world system , including:
- Voice pipelines (speech-to-text → LLM → text-to-speech)
- ML / GenAI behaviour (non-deterministic systems)
- Healthcare data integrations (FHIR, HL7, etc.)
- Real-time production environments
- Customer-specific deployments
What You’ll Own
- Lead and scale a team of ~8 QA Engineers SDETs
- Design a risk-based quality strategy across AI, data, and platform layers
- Build testing systems across:
- Functional automation
- Data pipelines / ETL validation
- Integration regression
- Production monitoring
- Define how to test LLM-driven workflows conversational logic
- Partner deeply with:
- Engineering
- Product
- Data / ML teams
- Customer implementation teams
- Improve:
- Release reliability
- Production triage
- Defect escape rates
- Customer-facing quality
- Launch readiness for enterprise customers
- Root cause analysis across complex system failures
- Embedding quality as a cultural standard , not a function
- 8 years in QA / Quality Engineering / SDET leadership
- Experience with complex, distributed systems (APIs, data, integrations)
- Exposure to ML / GenAI systems (or other probabilistic systems)
- Strong understanding of:
- Automation strategy
- Data validation ETL testing
- Release production quality
- Comfortable working with:
- SQL (strong)
- Python (working familiarity)
- Experience with modern data stacks:
- Databricks / Spark / DBT / Postgres
- Airflow or similar orchestration tools
- Healthcare / regulated environments
- Voice AI / conversational systems
- LLM testing / prompt-driven workflows
- Observability tooling (e.g. Datadog, Splunk)
- Experience in fast-scaling startups
- Releases become predictable and reliable
- Production issues drop — and are resolved faster
- Data quality is trusted across the platform
- QA evolves into a strategic function, not a gatekeeper
- The business has real confidence in AI behaviour customer outcomes
If the system fails:
- Conversations break
- Data becomes unreliable
- Patient experiences degrade
- Care teams scale effectively
- Patients feel heard and supported
- Healthcare systems operate more efficiently
Why Join
- Mission-driven: real-world impact on patient care
- Deep technical challenge across AI data real-time systems
- Strong funding rapid growth phase
- Opportunity to define quality in a category-defining product
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