Senior Research Scientist - Bayesian Optimization / Experimental Design or Causal Machine Learning

Key Skills

Algorithms, Analytical and Problem solving, Deep Learning, Design, Drug Discovery, Effective communication skills, Leadership Skill, Linear Algebra, Machine learning techniques, NeurIPS, Optimization, Python Programming

Job Description

About the role

We’re seeking an experienced Research Engineer 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 with strong engineering skills, including expertise in designing, implementing, improving, and deploying distributed machine learning systems at significant scale. In addition, we highly value proficiency with state-of-the-art machine learning algorithms and exceptional problem solving skills. In this role, you will:

• Support Valence Labs’ research agenda across ML for drug discovery.
• Engage with and contribute to open-source libraries developed by Valence and the research community.
• Create and improve novel ML methods that will accelerate drug discovery.
• Collaborate with an interdisciplinary team of dry and wet lab scientists to inform and improve our models and systems.
• 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.
• Strong programming skills and understanding of modern software development practices, especially in Python.
• Scientific knowledge of biology, chemistry, or physics along with previous experience working in a scientific environment across disciplines.
• Experience with real world natural science data and the associated challenges.
• Experience with high throughput bioassay data such as next-generation sequencing data, cellular imaging data, proteomics data or similar.
• Proven track record in machine learning, including multimodal 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.