About Limbic Limbic is the leading clinical AI company for mental healthcare and the largest deployment of patient-facing mental health AI in the world. Our technology has supported over half a million patients across the UK and US, helping health systems deliver higher-quality, more accessible, and more efficient care.

We regulated the first AI diagnostic model for adult mental health as a Class IIa medical device. We’ve published in top-tier journals, won major awards, and advise governments and regulators globally on AI safety, medical devices, and the future of clinical agentic systems.

For a Customer Support Engineer, this means joining a company where reliability and clarity matter. You’ll help keep mission-critical workflows running smoothly, make complex technical issues understandable for customers, and contribute to a product that directly improves patient care. It’s meaningful work with real-world impact — and you’ll sit at the center of how customers experience our technology.

Role Summary

Reporting to the Director of Customer Success (US) , this role sits within the CS organization but maintains a deep, day‑to‑day working relationship with Engineering and Product . You’ll be the connective tissue between customers, CS, and the technical teams — ensuring issues are understood, reproduced, validated, and communicated with clarity.

We're looking for a Customer Support Engineer who is equal parts customer‑facing communicator, technical troubleshooter, and product quality guardian. This person digs into JSON, logs, configs, and edge cases — while also helping customers feel supported and understood. They’ll partner with Engineering, Product, and Customer Success to resolve issues, validate fixes, identify patterns, and keep our AI systems running smoothly.

This role blends:

• Customer support 🗣️
• Light engineering/debugging 🛠️
• QA + testing ✔️
• Metrics and operational rigor 📊

You'll be a critical part of our support and reliability layer as we scale in the US.

Key Responsibilities

🧑‍💻 Customer Support & Troubleshooting

• Serve as a primary technical contact for customers when issues arise.
• Investigate problems using logs, JSON payloads, dashboards, and system traces.
• Reproduce issues, gather detailed context, and communicate clearly with both customers and internal teams.
• Provide timely updates and set realistic expectations.
• Help develop customer‑facing troubleshooting guides.

🔍 Technical Diagnostics & Debugging

• Dive deep into how our system handles voice, chat, triage flows, and routing.
• Review and interpret JSON, API outputs, and workflow configuration.
• Identify whether issues stem from configuration, logic, product behavior, or external integrations.
• Surface clear, actionable insights to Engineering.

🧪 QA, Testing & Validation

• Test new features, configurations, and fixes before they reach customers.
• Create and execute test cases for reliability and regression.
• Validate AI agent behavior, routing logic, and configuration accuracy.
• Help maintain internal test environments.

📊 Metrics & Operational Excellence

• Track incident trends, response times, and resolution quality.
• Build and maintain simple dashboards for support metrics.
• Identify patterns that point to product bugs, training gaps, or systemic issues.
• Recommend improvements to reduce support volume and increase reliability.

🤝 Cross-Functional Collaboration

• Work with Engineering on root‑cause analysis and bug reproduction.
• Partner with Product to identify systemic issues or improvement opportunities.
• Support Customer Success with troubleshooting, configuration checks, and customer escalations.
• Represent customer needs internally with clarity and precision.

Experience Ideal Qualifications

• 2–5+ years in technical support, product support, QA, or a hybrid support/engineering role.
• Experience reading JSON, logs, or debugging API‑driven systems.
• Background in SaaS, automation, AI or machine learning–powered systems, healthcare, or a technical support environment.

Skills

• Strong problem‑solving instincts and diagnostic ability.
• Ability to simplify complex technical behavior into clear customer explanations.
• Familiarity with API concepts, integrations, structured data, and foundational machine‑learning concepts (model behavior, inputs/outputs, inference patterns).
• Experience with QA tools, test cases, and validation workflows.
• Strong written communication.
• Calm under pressure; thrives when things break and need fixing.

Mindset

• Curious and willing to dig until the root cause is understood.
• Comfortable learning new systems quickly.
• Balances empathy with precision.
• Enjoys a mix of customer interaction and technical hands‑on investigation.
• Stays steady when things break, bringing clarity and structure to investigations
• Enjoyes improving reliability and reducing repeat issues, and improving reliability.

What Success Looks Like (6–12 Months)

• Issues are diagnosed quickly and communicated clearly.
• Repro steps and logs are consistently high‑quality for Engineering.
• Customers feel supported and confident in our system.
• Patterns are identified early and shared proactively.
• Support volume decreases as processes and systems improve.
• Testing and validation catch issues before customers do.