About the Role
As a Forward Deployed Engineer (FDE), you will operate between customers, customer success, product, and engineering. Your role is to bridge the gap between our core product and research teams and our customers. You will help customers adopt AI medical products, such as Scribes, Receptionists, and Coders, by understanding their workflows, configuring solutions, testing behavior, debugging issues, and translating customer needs back into product and engineering feedback. You will turn complex clinical workflows into effective deployments.
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This is a customer-facing engineering role ideal for someone who enjoys collaborating with both customers and builders. You should be comfortable working with APIs, integrations, AI-agent behavior, configuration systems, evaluations, and debugging, while also being able to communicate ideas clearly and build trust with customers.
Additionally, you will help us enhance our delivery methods for customers. You will absorb a constant stream of feature and customization requests as customers configure their agents and workflows. A critical part of this role is evaluating what to configure now, what to turn into repeatable templates or playbooks, and what to escalate as real product gaps, articulating these tradeoffs to both customers and engineering. Forward Deployed Engineers are closely aligned with customers, allowing them to recognize real operational pain and, in turn, recommend meaningful product improvements.
What You’ll Do
Deploy and Configure Customer Workflows
- Own hands-on setup for customer deployments across AI products.
- Configure workflows, routing logic, prompts, settings, and product behavior.
- Translate customer workflows into deployable configurations.
- Configure agent behavior and clinical scheduling rules against customer EHRs for voice and scheduling products.
- Test workflows end-to-end before and after launch.
- Identify gaps between customer needs and current product capabilities.
Support Technical Onboarding and Implementation
- Map customer systems, workflows, data flows, and integration requirements.
- Collaborate with engineering and implementation teams on testing, validation, and rollout.
- Investigate technical issues during onboarding and deployment.
- Create repeatable deployment playbooks and documentation.
- Help customers go live faster without compromising quality or safety.
Work Directly with Customers
- Participate in customer calls for technical clarification.
- Employ structured questioning to understand workflows, edge cases, and constraints.
- Communicate technical tradeoffs succinctly to non-technical stakeholders.
- Build trust with customer teams through precision and responsiveness.
- Assess when to solve, configure, or escalate issues.
Improve AI-Agent Quality and Reliability
- Test and evaluate deployed AI workflows using transcripts, traces, evaluations, and live testing tools.
- Debug configuration issues, product gaps, and unexpected agent behavior.
- Partner with AI and engineering teams for deeper investigation as required.
- Define safe and repeatable deployment practices for AI systems handling sensitive patient data.
- Highlight recurring issues that should inform product improvements.
Feed Product Signal Back to Engineering
- Identify patterns in customer requests.
- Document recurring pain points and missing capabilities.
- Recommend repeated customizations as product features, templates, or tooling.
- Assist engineering in understanding customer realities without needing constant involvement in customer calls.
What Good Looks Like
- Onboarding and configuration of products occur faster, reducing time-to-live for new deployments.
- Customer issues are resolved more efficiently due to a strong technical liaison.
- Deployed AI workflows demonstrate reliable performance in production, achieving higher task-completion rates.
- Manual work in agent configuration and customer operations is minimized through improved tools and processes.
- Customer needs are closely linked to product and engineering feedback mechanisms, decreasing engineering time spent in customer calls.
- Customer-facing teams benefit from a technical counterpart translating customer experiences into actionable signals for product development.
What We’re Looking For
Required
- Experience in software engineering or forward deployed engineering with a client-facing focus.
- Hands-on experience with AI products, AI agents, LLM workflows, or configuration systems.
- Ability to read code (primarily in TypeScript and Python), write SQL, understand APIs, and navigate technical workflows.
- Strong documentation and communication skills.
- Ability to translate complex customer workflows into clear technical requirements.
- High ownership and proactive attitude.
- Comfortable working directly with both clinical and non-technical stakeholders.
Strong Signals
- Background in AI agents, LLM, or automation tools.
- Familiarity with healthcare technology, EHRs, or clinical integrations (e.g., Athena, AdvancedMD, FHIR/HL7), with awareness of HIPAA/PHI.
- Startup or founder experience, adaptable to high-ambiguity environments.
- Knowledge of AI-native development tools for efficient project execution.
- Strong debugging instincts and customer empathy.
Who This Role Is Not For
This role may not be suitable if you:
- Prefer to work solely on core product engineering.
- Avoid customer interactions.
- Require fully specified tickets before taking action.
- Are uncomfortable with ambiguous situations.
- Seek a stable, fully-documented product without pushing to build necessary infrastructure.
- View every customer request as something to be manufactured by engineering.
- Tend to over-promise to circumvent difficult discussions.
Location
- Onsite - Media City
- Occasional travel to client sites.
