About us

Forus is building an AI-powered network that connects doctors, pharmacies, payers, and biopharma to accelerate new science to patients.

Our platform automates the complexity between a doctor prescribing a medicine and a patient starting treatment, including insurance coverage, financial assistance, and supply chain navigation. We provide this support free to doctors and patients, helping them get life-changing medicine faster, cheaper, and easier. The resulting network enables biopharma companies to design better research, launch new drugs more effectively, and invest in harder-to-treat conditions.

Forus has exceptional product-market fit and is growing rapidly through organic adoption

Accelerating scale: We already support patients across more than 85% of ZIP codes and partner with 9 of top 15 largest biopharma companies in the world.

AI-native, durable model: Our business becomes stronger as AI improves, as every new doctor, patient, and partner makes the network more valuable.

Backing to build for the long term: We raised $300M+ from leading investors to build an exceptional team and unlock the full scale of the opportunity.

Our number one priority is scaling to market demand. We are looking for individuals who are high horsepower, high throughput, and hyper resourceful to help us increase capacity and grow. We move fast and need to move faster.

If we succeed, science will be the only limit to medicine.

All full-time roles are in person in New York.

About the role

As a member of the Clinical Intelligence Review team at Generator Health, you will use your managed care expertise to optimize the quality, accuracy, and efficiency of our AI models and clinical workflows. You will own the review of all clinical artifacts our system produces – using cutting-edge AI tooling to build a view of accuracy that stitches together clinical context, payer requirements, and regulatory compliance. Using your clinical judgement and nuanced auditing, you will help build the clinical guidance our models and clinical teams use to continuously improve our system’s outputs.

This is a hands-on role centered on clinical review, structured feedback, and iterative improvement. You’ll look beyond individual cases to identify recurring problems, synthesize learnings into clinical guidance, and assess whether changes improve quality and throughput. We’re looking for someone who thrives in detailed clinical work and is excited to use AI to solve one of the most complex and time-consuming problems in healthcare operations.

This is a demanding role with a high level of autonomy and responsibility. You will be expected to act like an owner: prioritize effectively, surface problems early, and follow through until they’re resolved. If you are low-ego, hungry to learn, and motivated by consequential clinical work, we want to hear from you.

If you join, you will

Audit a high volume of prior authorizations and AI-generated clinical outputs for accuracy, completeness, and alignment with the medical record and applicable payer criteria.

Apply evidence-based clinical reasoning to complex or ambiguous cases, distinguish clinically meaningful errors from minor issues, and help build a system that scales your expertise.

Identify recurring error patterns, gaps in clinical guidance, and edge cases; investigate their underlying causes and recommend improvements to review standards and workflows.

Apply AI tools to clinical review and auditing, critically evaluate their outputs, and identify ways to increase throughput while maintaining a high standard of clinical quality.

Own the quality and timeliness of your work, meet review expectations, and follow issues through to resolution.

We’re looking for you if you have

A Doctor of Pharmacy (PharmD) degree and 2–10 years of professional experience, including hands-on managed care work in prior authorization, utilization management, appeals, or clinical quality at a health plan, pharmacy benefit manager (PBM), or similar organization.

Excellent clinical reasoning: you can synthesize medical records, medication histories, payer criteria, and clinical evidence, work through ambiguity, and clearly explain a defensible conclusion.