Deep Learning Researcher

About us

Our company is transforming neonatal nutrition therapy through clinically validated AI supported by publication in top journals such as Nature Medicine / Science Translational Medicine, and is underway for deployment  are backed by the NIH and leading venture capital firms. Our mission is to dramatically improve outcomes for the most vulnerable patients, newborns in NICUs, while simultaneously increasing efficiency and consistency for hospitals.

We believe that saving a newborn’s life isn’t just an immediate win; it represents decades of life preserved. Improving care at the very start of life can mean 70+ years of impact, a multiplier few areas of medicine can match.

This is a remote position, and the role suits someone who thrives with autonomy, moves fast, and wants a direct line of sight to patient impact. You’ll develop AI systems that discover treatment strategies from real-world clinical data, directly improving outcomes for critically ill newborns. This is a highly cross-functional role. You’ll interface with our software and Epic integration teams, collaborate with clinicians ranging from neonatologists to pharmacists, and work closely with advisors who have 100+ publications in AI for healthcare.

Beyond technical excellence, this role offers the chance to help redefine how neonatal care is delivered.

Responsibilities include:

• Design causal inference frameworks for treatment effect estimation from observational clinical data (confounding, time-varying treatments, censoring)
• Develop RL models for safe, deployable treatment policies
• Build dynamic treatment regime models optimizing sequential decisions across multi-week patient trajectories
• Own data pipelines, feature engineering, and model training on multi-site datasets
• Publish in top venues

Required Qualifications:

• Must reside within the United States (visa sponsorship is available for qualified candidates).
• MS or PhD in ML, Statistics, CS, or related quantitative field;
  Bachelor’s with significant research or development experience in industry or academia also considered
• First-author publications at top venues (NeurIPS, ICML, ICLR, AISTATS, UAI, JMLR, or clinical AI venues)
• Strong Python; fluency with PyTorch or Tensorflow
• Experience with time-series data

Preferred Qualifications:

• Experience in causal inference, RL, sequential decision-making, or dynamic treatment regimes
• Healthcare data experience (EHR, longitudinal clinical records)
• Prior work on treatment effect heterogeneity or counterfactual prediction
• Production ML/MLOps experience

Interested candidates should apply directly via the following link: (Use the "Apply for this Job" box below). Xkaa Zb DQB7

Rp8. Applications submitted through Linked In will not be considered.