Head of Engineering

Head of Engineering – AI & Healthcare Technology

• Own how the company uses AI models, including model selection, deployment, and cost optimization. Continuously improve the product as models evolve, using less expensive models where they meet the required quality standards.
• Build evaluation frameworks and quality feedback loops to demonstrate, with evidence, whether documentation is accurate, complete, and consistent, and whether new capabilities are ready for real-world clinical practices.
• Empower engineers and non-engineers to build and ship products using the best available AI models and development tools. Continuously improve team workflows as these tools advance, while maintaining high standards for the quality and security of customer-facing releases.

• Own end-to-end R&D delivery, translating ambiguous customer needs into reliable, production-ready software for a growing multi-tenant healthcare platform.
• Standardize phone system integrations to make onboarding new medical practices faster, more efficient, and repeatable.
• Maintain exceptionally high standards for correctness and data integrity across integrations with Electronic Health Records (EHRs), telephony systems, and third-party APIs.
• Understand and optimize the complete cost of product delivery, including telephony, transcription, and AI model usage, while designing for sustainable unit economics.

• Improve system reliability through robust observability, alerting, incident response processes, and deployment pipelines.
• Build security and compliance into the platform by default, including HIPAA compliance, encryption, and auditability, and establish standards for the engineering team to follow.

• Define the team's technical direction and build alignment around architectural decisions and engineering priorities.
• Hire exceptional engineers who raise the bar and foster a culture that combines AI-driven ambition with rigorous quality standards.
• Improve engineering capabilities through code reviews, design discussions, and collaborative programming.
• Partner with clinical and product stakeholders to translate requirements into sound, scalable technical solutions.

• Understand how nurses, healthcare providers, and practice staff rely on the platform in their daily workflows. Identify gaps, prioritize improvements, and ensure solutions are delivered effectively.
• Pay close attention to user experience details, including presenting information more clearly, reducing unnecessary clicks, and making the next action intuitive.
• Represent the customer's perspective within the engineering organization, ensuring the team remains focused on the people and workflows the product serves.

• You bring energy and urgency to the work, helping the team tackle meaningful and challenging problems every day.
• Each new generation of AI models improves the product, and adopting new models becomes a routine process rather than requiring a major rebuild.
• Product documentation quality is measured with evidence, and new capabilities reach customers only after meeting established quality standards.
• A small engineering team delivers an exceptional amount of work because AI is embedded throughout its development processes.
• New medical practices are onboarded through standardized, repeatable integrations, allowing the platform to scale across more practices and enterprise customers without compromising reliability or security.
• The cost of delivering the product decreases as usage grows.
• Engineers continuously improve through your feedback, standards, and established engineering practices, while new hires further strengthen the team.
• The CEO and the wider team trust you to make critical technical decisions, allowing others to focus on their respective priorities.

• 7+ years of experience building and owning production systems, including experience hiring and leading engineering teams.
• Hands-on experience building with Large Language Models (LLMs), not just building infrastructure around them. You have shipped LLM-powered features to production and managed post-launch challenges such as evaluation, quality drift, latency, and cost.
• Strong awareness of the latest AI developments. You actively experiment with new models and agentic tools, have a clear perspective on their future direction, and can distinguish genuine technological breakthroughs from hype.
• AI-first…