Head of QA

🚀 Head of Quality Engineering – Voice AI / Healthcare AI
Location:
Remote (U.S.) | Optional Bay Area presence
Comp:
$200k–$255k + equity + strong benefits
The Opportunity We’re partnering with a
high-growth AI healthcare company
building something genuinely different.
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

They’ve already processed
millions of real clinical conversations
, and are now scaling rapidly with backing from
top-tier investors and strategic healthcare players
.
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

👉 In short:
you’re defining what “quality” means in an AI-native healthcare platform.
🧠 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

You’ll also play a key role in:

- Launch readiness for enterprise customers
- Root cause analysis across complex system failures
- Embedding
quality as a cultural standard
, not a function

🛠️ What They’re Looking For

- 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

➕ Bonus Points

• Healthcare / regulated environments
• Voice AI / conversational systems
• LLM testing / prompt-driven workflows
• Observability tooling (e.g. Datadog, Splunk)
• Experience in fast-scaling startups

📈 What Success Looks Like

- 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

🎯 Why This Role Matters In this environment,
quality = trust
.
If the system fails:

• Conversations break
• Data becomes unreliable
• Patient experiences degrade

If it works:

• Care teams scale effectively
• Patients feel heard and supported
• Healthcare systems operate more efficiently

👉 You’re not just improving QA — you’re shaping
how AI interacts with people in healthcare
.
🌍 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