Senior AI Engineer

Senior / Principal AI Engineer (Generative AI, Multi-Agent Systems)

Location: New York City (Hybrid - 3 days onsite) Compensation: $200,000 - $300,000 + equity Contact / Apply: View email address on click.appcast.io

About Our Client

Our client is building AI systems that support real clinical workflows at scale—delivering high-quality medical reasoning, documentation, and triage while meeting the reliability bar required in regulated healthcare environments. This is a high-impact role for engineers who want to ship production AI that directly affects patient care.

The Role

We're hiring a Senior / Principal AI Engineer to build core AI systems, clinical data infrastructure, and safety layers that enable reliable, real-time medical AI. You'll work on multi-agent reasoning, RAG pipelines over large clinical knowledge bases, model optimization, and high-throughput inference—alongside infrastructure for EHR integrations and human-in-the-loop escalation. This is a high-ownership role suited for engineers who can move from research → production and design systems that are measurable, debuggable, and safe.

What You'll Build

Core AI Systems

• Multi-agent consensus architectures where specialized models debate cases before conclusions
• RAG pipelines processing thousands of clinical guidelines structured for LLM consumption
• Fine-tuning and model distillation to improve performance and efficiency
• Real-time inference systems supporting millions of medical consultations monthly
• Custom workflows for domain-specific medical reasoning

Clinical Data Infrastructure

• HIPAA-compliant NLP pipelines extracting structured data from patient conversations
• QHIN integrations for automated EHR data retrieval and reconciliation
• Intelligent triage systems routing between AI and human providers
• Documentation generation aligned to provider-specific protocols

Safety & Scale

• Emergency detection with sub-second escalation to human oversight
• Validation layers targeting 99%+ treatment plan accuracy
• Distributed systems handling rapid growth and peak loads
• Fail-safe mechanisms for critical path decisions in regulated environments

You may also work on voice/video AI interfaces, multilingual medical NLP, predictive health modeling, and infrastructure designed for the next billion patient interactions.

Responsibilities

• Design and ship production-grade LLM systems with strong evaluation, observability, and reliability guarantees
• Build multi-agent orchestration patterns (routing, tool-use, consensus, and failure handling)
• Develop retrieval + grounding layers (knowledge ingestion, indexing, ranking, citation/traceability)
• Improve model performance through fine-tuning, distillation, optimization, and latency/cost tradeoffs
• Implement safety systems: escalation paths, validation layers, monitoring, and guardrails
• Collaborate cross-functionally with product, clinical, and engineering partners to translate requirements into measurable systems

Required Qualifications

Education & Background

• CS degree or equivalent demonstrated expertise
• Hard science degrees (Physics/Math/Engineering) welcome with strong technical foundation
• Preference for graduates from top programs (where demonstrated rigor is clear)
• Preference for founders / founding engineers / substantial startup experience
• Independent problem solver who collaborates well cross-functionally

Technical Expertise

• 7+ years engineering experience with deep LLM / generative AI expertise
• Strong knowledge of foundation models (OpenAI, Anthropic, LLaMA-class models)
• Experience shipping production AI systems with real-time inference constraints
• Strong Python; experience with PyTorch and/or TensorFlow and modern AI frameworks
• Experience with prompt engineering, fine-tuning, and model optimization for specialized domains

Preferred Qualifications

• AI systems for healthcare, medical, or life sciences applications
• Familiarity with medical terminology, clinical workflows, and standards (HL7, FHIR)
• Experience with medical AI safety, bias detection, and fairness in healthcare
• Domain-specific NLP experience (classification, extraction, summarization)
• Worked directly with medical professionals or in regulated healthcare environments
• Published AI/ML research (especially healthcare or safety-critical)
• Seed/Series A experience in AI or healthcare

Why This Role

• Build systems that operate at the intersection of AI + safety + real clinical scale
• High ownership over architecture and reliability—shipping production AI, not demos
• Work on multi-agent reasoning, RAG, real-time inference, and healthcare integrations in one role
• Mission-driven impact: improving workflows and outcomes for patients and clinicians