Senior Technical Program Manager, Applied AI [NYC or SF]
$175k - $230kConcurrence
Job Description
Job Description
About Concurrence
Concurrence (formerly Amigo) is building the platform healthcare organizations use to build their own clinical AI agents. Our mission is to make excellent care accessible to every patient, not rationed by the limits of clinician time.
Underneath our platform is the Health World Model, which unifies a health system’s data into a living, event-level record of every patient’s care. On top of it, agents handle work across the patient journey, from intake and scheduling to medication management and clinical decision support.
Our agents have supported more than 10 million patient encounters and are on track to grow tenfold this year. We run natively on Databricks, integrate with every major EHR, and are backed by Madrona, General Catalyst, and Optum Ventures. We’re about 35 people, working in person in New York and San Francisco.
About this roleOur Applied AI team is a group of forward deployed engineers (FDEs). They take Concurrence from signed contract to production at our customers, usually across several accounts at once. Each account has its own data, integrations, clinical workflows, and deadlines. This role reports to the Head of Applied AI.
Our FDEs are excellent builders. What we need now is someone who makes the whole operation legible. That means knowing at any moment what every FDE is working on, what's at risk, what's blocked and why, and whether we'll hit what we've committed to.
This is a hygiene role in the best sense. If work isn't tracked in Linear, we can't plan capacity, judge workloads, measure on-time delivery, or help people get better at estimating and meeting dates. You'll make Linear the trustworthy source of truth for applied work. You'll do it with the follow-through, judgment, and technical credibility that make engineers see it as useful rather than bureaucratic.
You've been an engineer, and that matters. When an FDE says they're "blocked on platform," you need to know whether that's true. Is it a real platform gap, or applied-side work that hasn't been done? You need to be able to push back credibly in either direction.
Above all, you have relentless follow-through. You don't let things drop, you don't wait to be asked, and you don't stop chasing until it's done.
What you'll ownThe cross-account delivery plan. You'll own the plan across every active deployment: dates, dependencies, FDE capacity, and risk. You're the person who sees conflicts coming two weeks before they hit.
Linear as the source of truth. You'll design how Applied AI works in Linear and then hold the team to it. That includes the project structure per account, labels that separate applied blockers from platform blockers, an estimation scale, and a shared definition of done. The result: tickets are current, statuses are honest, and due dates are set and met.
Accountability on progress. You'll check in on work before it becomes a surprise. You'll chase updates, surface slips early, and make sure nothing goes quiet.
Capacity and workload. You'll know how loaded every FDE really is. That includes the unplanned work that never shows up on a roadmap, like production issues, customer escalations, and safety reviews on live agents. You'll build plans with buffer grounded in real data.
Fair delivery metrics. You'll measure on-time delivery, estimation accuracy, and how early risks get flagged. When something slips, you'll attribute it honestly to customer, platform, or applied causes, so people are judged on what they control.
The field-to-platform loop. You'll triage blockers between applied and platform, and turn field needs into well-specified platform tickets. You'll spot patterns across accounts: when three FDEs build the same workaround, that's a platform feature. You'll work with platform leadership on priority and make sure fixes make it back to the account.
Grounded commitments. You'll partner with our deployment strategists, who own the day-to-day customer relationship, so that the dates we give customers reflect real capacity.
A lightweight operating rhythm. Think modern, nimble scrum master, without the dogma. You'll run async status updates, a weekly cross-account review, and short retros when something slips. You'll automate the nagging, such as stale-ticket alerts, missing-date flags, and status rollups into Slack, so your time goes to judgment rather than chasing.
It isn't customer-facing account management. Deployment strategists own that, and you'll work closely with them.
It isn't owning the technical plan inside an account. Our Applied AI tech leads own architecture and execution within their deployments. You own the system across accounts and the interface with platform.
It isn't ceremony for its own sake. If a meeting or process isn't producing better decisions, you kill it.
You've shipped software as an engineer before moving into program management, and you can still read code, follow a system design discussion, and reason about APIs, data pipelines, integrations, and LLM-based systems well enough to evaluate a blocker yourself.
You've run delivery as a technical program manager, engineering manager, or delivery lead, including across multiple concurrent teams, customers, or deployments.
You've built a team's execution system from scratch, not just operated one someone else designed.
You're deeply fluent in Linear or a comparable tool, and you've used its API, integrations, or automations to make tracking cheaper for everyone.
You've measured delivery and estimation in a way engineers trusted, and you know how to keep those metrics from being gamed.
You earn engineers' respect rather than their tolerance. You're direct, you ask the uncomfortable question, and people still want you in the room.
You have relentless follow-through. This is the job.
Experience in healthcare or health IT, such as EHR integrations, hospital security reviews, clinical validation cycles, or HIPAA environments
Experience in a forward deployed, solutions engineering, or professional services organization
Familiarity with Databricks or other modern data platforms
Health & Wellness
Comprehensive health, dental, and vision insurance
Daily catered lunch and dinner
Mental health support and wellness coaching
Flexible wellness stipend for fitness, therapy, or personal growth
Annual learning budget for courses, books, or conferences
Conference attendance budget for professional development
Annual team offsite
Academic collaboration opportunities
Unlimited PTO
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.
Low ego: Politics and territory don't interest you. The best ideas win, regardless of who has them.
Direct: You say the hard thing, challenge ideas openly, and commit fully once decided.
High agency: You thrive on trust rather than instruction. When you see something is broken, you fix it. You don’t file tickets and wait for someone else.
Bar of excellence: You hold yourself to a bar most people wouldn't, and you want teammates who do the same.
Skeptical: You push back on rules that don’t make sense and question assumptions that haven’t earned their place.
Compensation Range: $175K - $230K
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