Member of Technical Staff, Research Engineer

About the role

Clinical AI research aims at disease predictability using multi-modal methods. The team builds tools that help physicians select treatments and integrate into clinical workflows. You will join a research and deployment effort led by recognized clinical AI and clinical experts.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Production-ready code transforms machine learning and statistics papers into clinical tools that physicians and researchers use directly. Model evaluation frameworks measure reliability and performance across clinical settings to monitor outcomes. Data pipelines integrate and preprocess data while maintaining consistent documentation for downstream analysis. GPU clusters run code efficiently, emphasizing speed and scalability for clinical workloads. Cloud inference pipelines deploy machine learning models to keep clinical tools performant and reliable in production. Regression and unit tests maintain code stability and safety for clinical use and ongoing development. Results dissemination occurs through co-authored research papers and abstracts shared with medical and research communities. Multidisciplinary collaboration aligns models, clinical insights, and deployments among engineers, scientists, and clinical partners.

Requirements

A BS/MS/PhD in computer science, machine learning, or statistics provides foundational knowledge for the role. Deep understanding of core machine learning concepts covers model behavior, training dynamics, and evaluation methodologies. Foundations of statistics, linear algebra, and probability guide method selection and interpretation of experimental outcomes. Python and PyTorch skills support model development, training, and debugging in research and production. Data visualization skills communicate complex results clearly to both technical and non-technical audiences in clinical and research contexts. Computer architecture knowledge enables parallel training of AI models and GPU optimization for large-scale clinical data. Deep learning experience ensures familiarity with architectures, training techniques, and deployment patterns for neural models. At least one area such as self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability, or computational pathology strengthens contributions to research problems. Detail orientation drives tasks to completion, ensuring rigor, reproducibility, and correctness in methods and experiments. A record of research publications in top-tier conferences demonstrates engagement with the field and peer-reviewed impact.

Nice to have

Experience in self-supervised learning, survival analysis, multi-modal learning, domain adaptation, causal inference, model interpretability, or computational pathology. Publications in top-tier conferences that highlight innovation in clinical AI and methodological rigor.

Skills & tools

Python, PyTorch, GPU clusters, cloud deployment, model evaluation frameworks, data integration pipelines, and scientific documentation tools support the research lifecycle.

Practical notes

Work is conducted in a clinical AI research lab with a flat organizational structure. New York HQ is the primary location, and the role is full time. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Clinical AI and precision medicine rely on methods such as self-supervised learning, survival analysis, multi-modal models, and causal inference. The field emphasizes reproducibility, scalability, and collaboration across research and clinical teams. Work targets real-world clinical tools that support decision-making for physicians. Methodological rigor and clear documentation enable trustworthy deployment in healthcare environments.