Senior Machine Learning Engineer

Charlie Health•New York, NY

2h•$170,000 - $220,000•Hybrid

About The Position

Charlie Health leads the nation in high-acuity virtual behavioral care, having delivered life-saving treatment to more than 100,000 clients nationwide. Our Matching and Outcomes team builds products to measure and improve clinical outcomes, including provider recommendation systems for clients and clinical decision support tools, enabling durable and effective treatment. You'll create predictive models and workflows to match patients with the best-fit therapists and peers, and enable clinicians to tailor care to each client's individual needs. The systems you build will help clinicians condense and synthesize knowledge across our clients and providers into actionable guidance at every moment. If you care about using data-driven insights to improve outcomes, this team is for you.

Requirements

A bachelor’s degree in a technical field, plus 4+ years of professional experience developing and delivering production software, including machine learning models; OR
A master’s or doctorate degree in a technical field, plus 2+ years of professional experience developing and delivering production software, including machine learning models.
Expertise in Python for ML (pandas, sklearn, etc.) and backend server development (FastAPI)
Strong SQL skills and experience navigating analytical and transactional databases to assess and curate high-quality datasets
Excellent at managing ambiguity—able to break down big, messy problems into smaller parts with tractable solutions and clear iterations
Growth mindset and sense of humor; you welcome feedback, adapt quickly in a fast-paced environment, and foster a culture of learning and fun
This role requires 4 days per week in our NYC office (Flatiron District). The team is entirely based out of NYC.

Nice To Haves

Experience developing agentic AI workflows is a plus

Responsibilities

Design, train, and evaluate machine learning models and AI systems that drive meaningful business impact
Identify opportunities to introduce or improve data-driven algorithms and automated solutions in partnership with clinical, design, data science, and product teams
Pioneer new approaches to measure and validate outcomes impact through advanced experimentation and causal inference techniques
Utilize ML infrastructure to serve model inferences in real time using live data streams
Write production code within a backend Python monolith to invoke ML predictions.
Foster a culture of collaboration and learning across engineering, product, and design through mentoring, documentation, presentations, and knowledge sharing