Ph.D. in Machine Learning, Computer Science, Statistics, or related field with 8+ years experience shipping ML products, and 3+ years leading ML teams in startups.
Proven track record building and scaling high-performing data science and ML engineering teams in resource-constrained environments.
Deep technical expertise in production ML systems and data infrastructure, including hands-on experience with data warehousing and model performance evaluation.
Experience in healthcare or regulated industries where model decisions impact real-world outcomes.
Responsibilities
Build and mentor a high-performing AI/ML team, establishing standards for technical excellence and career development.
Define and execute the ML roadmap while collaborating with Engineering to develop data warehousing solutions and ML infrastructure.
Contribute to modeling, evaluation, and analysis, ensuring high-quality ML systems deliver clinical impact.
Translate complex clinical workflows into ML opportunities while communicating model performance to technical and non-technical stakeholders.
Benefits
Collaborative and innovative work environment focused on making impactful changes in healthcare.
Opportunity to work at the forefront of AI in healthcare with a hands-on approach.
Recognition for contributions to a mission-driven team that aims to save lives.
Full Job Description
Director of AI/ML
In Brief
Lead Bayesian Health's AI/ML organization with a hands-on, scrappy approach: setting technical vision, rolling up your sleeves on critical modeling work, and building a world-class team that ships breakthrough ML products saving lives in hospitals nationwide.
What you'll do
As Director of AI/ML, you'll set the technical vision and strategy for Bayesian Health's machine learning organization while building and leading a high-performing team of data scientists and ML engineers. You'll partner deeply with Engineering to architect scalable data warehousing and ML infrastructure that enables rapid model development and reliable production deployment. At our stage, you'll also roll up your sleeves on critical IC work: prototyping models, evaluating system performance, and debugging production issues. This role requires thriving in scrappy, early-stage environments where you're building the plane while flying it, translating clinical needs into technical roadmaps, and getting your hands dirty to ship breakthrough healthcare products.
Responsibilities
Team Leadership: Build, mentor, and scale a world-class AI/ML team, establishing technical standards, career development frameworks, and a culture of excellence and ownership.
Technical Vision & Infrastructure: Define and execute the ML roadmap while partnering closely with Engineering to architect data warehousing solutions, ML infrastructure, and data pipelines that enable the team to rapidly prototype and deploy models at scale.
Hands-On Modeling & Evaluation: Contribute directly to critical modeling, evaluation, and analysis work, from studies to model performance experiments, ensuring the team ships high-quality ML systems that deliver measurable clinical impact.
Cross-Functional Partnership: Collaborate with Engineering, Product, and Clinical to translate complex clinical workflows into ML opportunities, and communicate model performance and impact to technical and non-technical stakeholders including customers and investors.
Minimum qualifications
Ph.D. in Machine Learning, Computer Science, Statistics, or related field with 8+ years shipping ML products, and 3+ years leading ML teams at early stage startups
Proven track record building and scaling high-performing data science and ML engineering teams in resource-constrained, scrappy environments.
Deep technical expertise in production ML systems and data infrastructure, including hands-on experience with data warehousing, real-time prediction, model monitoring, and performance evaluation.
Experience working with healthcare or similarly regulated industries where model decisions have high-stakes real-world consequences.
Preferred qualifications
Experience leading ML organizations through 0-1 product development in healthcare or clinical settings, thriving in environments with limited tooling and infrastructure.
Hands-on experience with clinical data standards (HL7, FHIR, EHR) and healthcare ML challenges including data quality, time-series forecasting, and anomaly detection.
Strong technical background in data platform architecture, including modern data warehousing solutions (Snowflake, Databricks, Redshift), streaming data systems, and ML infrastructure tools.
Track record of publishing research, speaking at conferences, or contributing to the broader ML community while delivering business results.
You bring passion and enthusiasm to your work, and are excited to join a growing team to Get Stuff Done and save lives!
