7 часов назад
Research Scientist (Machine Learning)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Research Scientist (Machine Learning) (AI-driven drug discovery): Building greenfield machine learning models and algorithms for predictive and generative drug design with an accent on deep learning architectures, experimental analysis, and computational biology and chemistry. Focus on developing scalable training and inference frameworks, solving complex modelling problems, and communicating research across scientific disciplines.
Location: London, United Kingdom; hybrid work with three days per week in the office
Company
develops AI models and drug design systems to accelerate scientific discovery and the development of new medicines.
What you will do
- Design and develop machine learning algorithms and models for drug discovery.
- Identify novel techniques and prepare the data required for model training.
- Develop model architectures and training algorithms, and analyse experimental results.
- Implement and scale training and inference engineering frameworks.
- Present research findings to machine learning scientists and specialists from other disciplines.
- Depending on experience, lead research projects, mentor scientists, and provide project supervision or management.
Requirements
- PhD or equivalent practical experience in a technical field.
- Proven experience applying deep learning, including architecture design, experimentation, analysis, and visualisation.
- Strong knowledge of linear algebra, calculus, and statistics.
- Experience with JAX, PyTorch, or TensorFlow, plus scientific tools such as NumPy, SciPy, or Pandas.
- Passion for applying machine learning research to real-world problems.
- Ability to work from the London office three days per week.
Nice to have
- PhD in machine learning or computer science, postdoctoral experience, publications, or contributions to machine learning codebases.
- Experience working across biology, chemistry, or physics, including biological or chemical data and software.
- Experience with real-world datasets, machine learning accelerators, large-scale deep learning, generative models, graph neural networks, drug discovery, computer vision, robotics, or applied reinforcement learning.
Culture & Benefits
- Interdisciplinary collaboration between machine learning specialists, engineers, and drug discovery scientists.
- Hybrid work designed to support knowledge sharing and in-person relationships.
- Culture guided by curiosity, creativity, integrity, determination, and collaboration.
- Commitment to equal employment opportunities and workplace accommodations.
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