Director of AI/ML

Director of AI/ML

In Brief

We're an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives.

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.

Who We Are

Bayesian Health's mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We're a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data.

We're funded by top tier tech and biotech investors: Andreessen Horowitz, American Medical Association's venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year. Read more about our recent publication in Nature Medicine that associates our products with lives saved.

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.