Founding Full Stack Engineer

• Practice Development & Integration

• Technologies Development Skills

• Location New York, United States

• Type Permanent

Founding AI Engineer – Oncology AI Platform (SF, In-Person)

Why This Role Is Unique

• 🎯 High-impact, early seat – As hire #7, you'll help architect the intelligence behind a first-of-its-kind AI platform focused on transforming a deeply complex domain of healthcare. You’ll shape product and engineering culture from the ground up.

• 📊 Unprecedented data advantage – We work with a proprietary dataset comprising 400M+ radiology, pathology, genomic, and clinical records from leading U.S. institutions.

• 🤖 Frontier-model access – You'll work directly with some of the most advanced model architectures available today, including pre-release access to models from top AI labs, integrated into a highly specialized medical-AI framework.

• 💰 Massive market, urgent mission – The opportunity sits at the intersection of a multi-trillion-dollar industry and a global health imperative. You'll have the technical canvas to make a significant dent in both.

• 🧠 Elite, execution-driven team – Collaborate with accomplished founders (including a national champion athlete and ex-intelligence CTO), world-class engineers, and a board of top oncologists from institutions like Johns Hopkins, UCLA, and MSK.

What You’ll Build (First 3 Months)

• Architect and deploy multi-agent AI workflows that synthesize structured and unstructured data to produce end-to-end treatment insights.

• Own post-training pipelines for cutting-edge LLMs (LoRA, RLHF, RLAIF), tailoring frontier models with medical expertise.

• Develop reward models focused on safety, factuality, and outcome-relevance in oncology.

• Prototype temporality-aware embeddings to enable longitudinal reasoning across patient timelines.

• Expand a bespoke evaluation system with synthetic vignettes, offline policy metrics, and adversarial probing.

• Integrate deterministic tools and perception models into reasoning chains (e.g., calculators, image recognition, structured decision aids).

• Build and refine retrieval and verification scaffolding, including KAG/RAG modules and factuality-checker agents.

Tough Problems You'll Tackle

• Designing agentic planners that chain together reasoning trees across planning, execution, retrieval, and decision support.

• Training reward models where signal is sparse, noisy, or contradictory.

• Encoding real-world EHR timelines in a way that preserves nuance and disease progression over time.

• Creating self-improving systems that turn system failures into new, high-quality training data—without relying on human labeling.

Who You Are

• 2+ years of experience building from 0→1 with strong full-stack fluency (backend and frontend).

• Comfortable in ambiguity, motivated by technical ownership, and driven by real-world impact

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