Senior Research Scientist

2 weeks ago

London, Greater London, United Kingdom Valence Labs Full time
Senior Research Scientist - Bayesian Optimization / Experimental Design or Causal Machine Learning

About Valence Labs

Valence Labs is an AI research and productization engine within Recursion dedicated to industrializing scientific discovery to radically improve lives. Combining the intellectual freedom of academia with the resources and stability of industry, our focus is the development of highly-autonomous systems that will spearhead a fundamental shift in the way treatments are discovered and developed for complex disease. Our research is driven by optimism, purpose, and a shared vision for a healthier tomorrow. We publish in top journals and conferences, are deeply committed to open-science and open-source, and maintain some of the largest and most active research communities in our industry. Our team is located in London and Montreal, where we share close connections with Mila, the world's largest deep learning research institute.

About the role

We're seeking an experienced Research Scientist to shape and lead the development of software and AI systems that will help in our mission of industrializing scientific discovery to radically improve lives. We're looking for individuals who possess the ability to own, pursue, and clearly communicate a research agenda, including carrying out long-running projects independently or in a team. This multifaceted role combines the need for a deep understanding of state-of-the-art machine learning algorithms, exceptional problem-solving and engineering skills, and experience in the computational life sciences. In this role, you will:

  • Join one of Valence Labs' Research Units, driving ambitious frontier research within the domain of ML for drug discovery.
  • Lead new research projects at the intersection of ML and drug discovery, leveraging your extensive expertise to devise, evaluate, and help deploy innovative and field-leading ML solutions.
  • Collaborate with researchers and engineers both internally and externally to identify unique challenges and opportunities for ML in drug discovery.
  • Work in a highly supportive, interdisciplinary environment, providing the intellectual freedom of academia with the resources and stability of industry.
  • Collaborate with biology researchers to run algorithmically designed wet lab experiments.
  • Present and communicate research findings through talks, blog posts, publications, and conferences.

A successful candidate will have most of the following:

  • PhD or Master's degree with 3+ years of industry experience.
  • Advance practical and theoretical knowledge of Bayesian optimization, active learning / experimental design and / or causal inference.
  • Strong programming skills and understanding of modern software development practices, especially in Python.
  • Scientific knowledge of biology or chemistry along with previous experience working in a scientific environment across disciplines.
  • Proven track record in machine learning, including multi-modal learning, designing new architectures, hands-on experimentation, analysis, visualization, and model deployment.
  • Demonstrated capability to understand and summarize scientific content and implement deep learning models based on descriptions from publications.
  • Strong knowledge of linear algebra, calculus, and statistics.
  • Passion for applying ML research to real-world problems.

Nice to have:

  • Experience in building and deploying high-performance implementations of deep learning algorithms.
  • Authorship of publications in peer-reviewed conferences (e.g., NeurIPS, ICML, or ICLR) or journals (e.g. Nature, Science, JACS, or ACS)
  • Contribution to high-visibility ML codebases.

Valence Labs is committed to creating a diverse and inclusive environment, where understanding and accommodating personal needs and preferences is a priority. Join our multidisciplinary team of passionate researchers, eager to push the boundaries of ML research and contribute to industrializing scientific discovery to radically improve lives.

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