Senior Engineering Manager

About the role

Scan.com is building a national imaging network that connects patients, providers, and payers to faster, higher‑value diagnostic imaging. As a Senior Engineering Manager you will lead a cross‑functional engineering team while staying hands‑on, contributing roughly 30% of your time to code and technical decisions.

Key responsibilities

• Lead and develop engineers across multiple product domains, owning velocity, process, performance, technical growth and culture.
• Remain a consistent technical contributor by writing production code, leading design reviews and pairing on complex problems.
• Define and enforce engineering standards for code quality, system design, testing, deployment and incident response.
• Drive an AI‑native operating model: identify where AI agents can replace or accelerate workflows, build evaluation frameworks and coach engineers to use AI as a primary tool.
• Partner with Product, Operations and Data to refine roadmaps, push back on scope when needed and ensure focus on high‑leverage problems.
• Own the architecture of your team’s systems, making build‑vs‑buy decisions, managing technical debt and ensuring scalability.
• Contribute to Scan.com’s broader engineering culture through interview loops, cross‑team design reviews and engineering all‑hands.

Required profile

• Proven experience managing and growing engineering teams in a startup environment.
• Genuine AI‑native fluency: deployed AI agents in production, built evaluation frameworks and coached teams to use AI as a primary tool.
• Active technical contributor with strong engineering fundamentals, writing production‑quality code in a modern backend or full‑stack language.
• Experience in a scaling healthcare technology company is a strong plus.
• Strong cross‑functional instincts, able to partner effectively with Product and Operations.
• Track record of growing engineers and helping them advance in their careers.
• High ownership orientation, treating business outcomes as personal responsibility.

Required skills

• AI agents deployment
• Evaluation framework design
• Production code development
• System design and architecture
• Observability and monitoring
• Deployment practices
• Incident response