Software Engineer

Maven Clinic is the world's largest virtual clinic for women and families on a mission to make healthcare work for all of us. Through Maven Enterprise, the company partners with more than 2,300 employers and health plans to provide end-to-end women's and family health programs spanning fertility and family building, maternity and newborn care, parenting and pediatrics, and menopause and midlife — improving clinical outcomes, reducing healthcare costs, and expanding equitable access to high-quality care at scale. Through its consumer platform, Maven provides direct access to virtual care across 30+ specialties, as well as dedicated hormone and GLP-1 care programs purpose-built for women. Founded in 2014 by CEO Kate Ryder, Maven Clinic has raised more than $425 million from leading healthcare and technology investors including General Catalyst, Sequoia, Dragoneer Investment Group, Oak HC/FT, StepStone Group, Icon Ventures, and Lux Capital. Recognized for innovation and industry leadership, Maven has been named to the TIME100 Most Influential Companies, CNBC Disruptor 50, Fast Company's Most Innovative Companies, and FORTUNE Best Places to Work. Learn more at mavenclinic.com

An award-winning culture working towards an important mission –  Maven Clinic is a recipient of over 30 workplace and innovation awards, including: 

• TIME 100 Most Influential Companies (2023, 2026)
• Fortune Change the World (2024)
• CNBC Disruptor 50 List (2022, 2023, 2024)
• Fortune Best Workplaces for Millennials (2024)
• Fortune Best Workplaces in Health Care (2024)
• Fast Company Most Innovative Companies (2020, 2023)
• Fortune Best Workplaces NY (2020, 2021, 2022, 2023, 2024)

As a Staff ML/AI Engineer at Maven, you will:

• Design, build, and deploy end-to-end AI-powered solutions using AI models (e.g., OpenAI, Gemini) and modern orchestration techniques such as RAG, multi-call prompting, and agent frameworks to power intelligent product experiences.
• Work with product, data, and engineering teams to turn rich healthcare data into actionable insights and intelligent workflows that improve the care journey for both members and providers.
• Develop robust ML pipelines and platform components that enable model experimentation, fine-tuning, training from scratch when needed, and deployment in production environments — with a strong focus on scalability, safety, and performance.
• Collaborate cross-functionally to prototype and productionize AI-driven features such as personalization, care routing, decision support, and summarization.
• Drive architecture discussions and technical decision-making across AI/ML systems, promoting best practices in coding, design, and evaluation of AI models.