Senior Software Engineer - Full Stack [NYC or SF]

• Building real-time interfaces for clinical oversight of live agent conversations
• Owning a feature end to end, from schema to API to UI
• Designing tools that let non-engineers shape agent behavior safely
• Making healthcare data explorable through natural language
• Hardening the reliability and performance of applications clinicians depend on
• Working with designers, clinicians, and customers to get the details right, * Daily catered lunch and dinner
• Mental health support and wellness coaching
• Flexible wellness stipend for fitness, therapy, or personal growth

Growth & Development

• Annual learning budget for courses, books, or conferences
• Conference attendance budget for professional development
• Annual team offsite
• Academic collaboration opportunities
• Unlimited PTO

• You’ve shipped production full-stack features end to end and owned the outcome
• You’re strong across the stack, from data modeling and APIs to modern front-end work
• You sweat correctness and reliability, especially where sensitive data is involved
• You have good product instincts and care how things feel to use
• You’re low ego, direct, and hold yourself to a high bar
• You can work on site in New York City or San Francisco, * Experience in a regulated or critical, low margin for error domain (healthcare, finance, legal)
• Experience and familiarity with WebSockets, SSE client and server implementations
• Background with design systems or complex UI state
• Experience with production AI or ML products

At Concurrence, we’re building the clinical AI infrastructure that health systems, life sciences companies, payors, and government agencies run their patient-facing care on. Our agents work across the whole patient journey: pre-visit intake, care navigation, post-visit care plans, and ongoing monitoring. Our mission is to make excellent care accessible to every patient, not rationed by the limits of clinician time.

This hasn’t been solved because the underlying record was never built for it. EHRs were designed for billing, not for systems that learn. So instead of layering another point solution on top, we’re rebuilding the medical record as a timeline of clinical events and running agents on top of it that improve with every patient interaction. Unlike single-purpose chatbots or scribes, Amigo is the platform our customers use to build their own agents. That means our work has to hold up across specialties, workflows, and regulatory environments and not just one narrow use case.

Our agents have supported more than 10 million patient encounters and are on track to grow tenfold this year. Our work is validated through partnerships with leading academic medical institutions. We’re fresh off our Series A, backed by Tier 1 VCs such as Madrona, General Catalyst, and Optum Ventures.

We’re a small team of around 35 people, working in person in New York City and San Francisco. We care deeply about craft, clinical rigor, and the speed at which good ideas reach patients. If you want your work to be measured in outcomes rather than releases, we’d like to hear from you., * Patients Win, We Win If patients aren’t getting better care, we haven’t earned the right to scale. Every internal decision gets pressure-tested: does this make patients’ lives better? If we can’t draw the line, we question why we’re doing it.

• High Standards, High Care We hold a high bar for the team because patients are counting on us to get this right. But high standards only work with genuine investment in each other. You can take risks, admit mistakes, and challenge ideas-not despite our standards, but because of them.

• Thoughtful Urgency We move fast by default, but speed without judgment is recklessness. The discipline is knowing which decisions are reversible vs. not. In healthcare AI, the companies that win will be fast everywhere they can be and careful everywhere they must be. We build the muscle to do both.

• Intensely Measured We instrument patient outcomes, provider ROI, system performance, and clinical accuracy. But data without action is surveillance. Every metric should have an owner, a threshold, and a response plan. If we’re measuring something but never acting on it, we stop measuring it.