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