Senior AI/ML Engineer - Healthcare Platform

Maven; New York (NY); Hybrid; USD 220,000 - 300,000; Full time

Benefits offered by this job

Hybrid work model

Parental leave

Personal career coach

Professional development stipend

401K matching

Job summary

Maven Clinic is seeking a Staff ML/AI Engineer to design, build, and deploy AI-powered solutions using foundation models and modern orchestration techniques to power intelligent product experiences.

You will collaborate with product, data, and engineering teams to turn rich healthcare data into actionable insights, and develop scalable ML pipelines for model experimentation, fine-tuning, and production deployment.

Qualifications

• Bachelor's or Master's in Computer Science or related field, or equivalent experience.
• Experience with foundation models and AI orchestration for product features.
• Skills in Python and backend or platform engineering for scalable systems.
• Familiarity with health data and patient/provider workflows is a plus.

Responsibilities

• Design, build, and deploy AI-powered solutions using foundation models and orchestration.
• Collaborate with product, data, and engineering to turn healthcare data into insights.
• Develop robust ML pipelines enabling experimentation, training, and production deployment.
• Prototype and productionize AI features such as personalization and care routing.
• Drive architecture decisions and promote best practices in AI systems.
• Mentor engineers and data scientists to grow AI fluency across the org.

Skills

Python

Backend development

ML/AI platform

Foundation models

RAG

LangChain

Vector search

Communication

Mentorship

Education

Bachelor's or Master's in Computer Science or related field

Tools

OpenAI

Vertex AI

LangChain

Vector search

Job description

Maven Clinic is seeking a Staff ML/AI Engineer to design, build, and deploy AI-powered solutions using foundation models and modern orchestration techniques to power intelligent product experiences.

You will collaborate with product, data, and engineering teams to turn rich healthcare data into actionable insights, and develop scalable ML pipelines for model experimentation, fine-tuning, and production deployment.