🚀 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