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2 часа назад

AI Research Scientist (Bayesian Statistics)

100 000 - 300 000$
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
US
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR
AI Research Scientist (Bayesian Statistics): Designing and implementing novel Bayesian methods for clinical AI research with an accent on uncertainty quantification and model explainability. Focus on translating machine learning research into production-ready code and co-authoring papers for top-tier conferences.

Location: New York, USA

Salary: $100,000 – $300,000

Company

hirify.global is a clinical research lab developing multi-modal AI methods to predict patient outcomes and assist physicians in precision medicine.

What you will do

  • Design and implement novel Bayesian statistics methods.
  • Translate machine learning research papers into production-ready code.
  • Build robust model evaluation frameworks.
  • Disseminate research results by co-authoring papers and abstracts.
  • Collaborate with a multidisciplinary team of engineers and scientists.
  • Co-mentor junior team members.

Requirements

  • PhD degree in statistics or machine learning.
  • Excellent knowledge of Bayesian statistics, including Gaussian processes and Bayesian clinical trial design.
  • Strong understanding of core machine learning concepts, linear algebra, and probability.
  • Excellent programming skills in Python and PyTorch.
  • Experience applying Bayesian statistics to uncertainty quantification in deep learning.
  • Must be eligible to work in the US (visa sponsorship available for exceptional candidates).

Nice to have

  • Publications in A* conferences (ICML, ICLR, NeurIPS, CVPR) or top-tier statistics journals.
  • Experience in self-supervised learning, survival analysis, multi-modal learning, or computational pathology.

Culture & Benefits

  • Comprehensive medical, dental, and vision insurance.
  • 401(k) plan with matching.
  • Generous equity package.
  • Commitment to rapid career advancement in a flat organizational structure.

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