Senior AI Engineer

Doctronic•New York, NY

13d•Hybrid

About The Position

Let's make healthcare free and accessible to everyone through AI. We're building an AI doctor that already handles millions of consultations - our goal is to scale this to billions while maintaining medical safety and accuracy. You'll also work on voice/video AI interfaces, multilingual medical NLP, predictive health modeling, and infrastructure for the next billion patient interactions.

Requirements

CS degree OR equivalent demonstrated expertise
Hard science degrees (Physics, Math, Engineering) welcomed with strong technical foundation
Preference for graduates from top programs at top universities
Preference for founders, founding engineers, or extensive startup experience
Independent problem solver who collaborates well cross-functionally
7+ years engineering with LLM and generative AI expertise
Deep knowledge of OpenAI GPT, Anthropic Claude, Meta LLaMA, and foundation models
Production AI systems with real-time inference requirements
Python, TensorFlow/PyTorch, modern AI frameworks
Prompt engineering, fine-tuning, model optimization for specialized domains

Nice To Haves

AI systems for healthcare, medical, or life sciences applications
Medical terminology, clinical workflows, healthcare data standards (HL7, FHIR)
Medical AI safety, bias detection, fairness in healthcare
NLP for domain-specific applications
Work with medical professionals or regulated healthcare environments
Published research in AI/ML, especially healthcare or safety-critical applications
Seed/Series A experience in AI or healthcare
Advanced degree in CS, AI/ML, or related field

Responsibilities

Core AI Systems
Multi-agent consensus architectures where specialized models debate cases before conclusions
RAG pipelines processing thousands of clinical guidelines built for LLM consumption
Fine tuning and model distillation to support efficient performance
Real-time inference systems handling millions of medical consultations each month
Custom workflows for domain-specific medical reasoning
Clinical Data Infrastructure
NLP pipelines extracting structured data from patient conversations (HIPAA-compliant)
QHIN integrations for automated EHR data retrieval and reconciliation
Intelligent triage systems routing between AI and human providers
Documentation generation matching provider-specific protocols
Safety & Scale
Emergency detection with sub-second escalation to human oversight
Validation layers ensuring 99%+ treatment plan accuracy
Distributed systems handling rapid growth and peak loads
Fail-safe mechanisms for critical path decisions in regulated environments