Machine Learning Researcher with Expertise in Generative AI and Structural Biology - London
Valence Labs
Date: 2 weeks ago
City: London
Contract type: Full time
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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 Machine Learning Researcher 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 machine learning systems. In addition, we highly value proficiency with state-of-the-art machine learning algorithms and exceptional problem solving skills. In this role, you will:
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 Machine Learning Researcher 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 machine learning systems. In addition, we highly value proficiency with state-of-the-art machine learning algorithms and exceptional problem solving skills. In this role, you will:
- Develop and implement generative models (e.g., diffusion models, flow matching, score-based models).
- Collaborate with researchers to push forward projects involving molecule, protein, and 3D structure generation.
- Stay abreast of the latest advancements in generative AI and apply them to our structural data.
- Collaborate with an interdisciplinary team of dry and wet lab scientists to inform and improve our models and systems.
- Engage with and contribute to open-source libraries developed by Valence and the research community.
- Present and communicate research findings through talks, blog posts, publications, and conferences.
- PhD or Master's degree with industry experience.
- Demonstrated expertise in generative models and experience applying these to structural biology or material science.
- Proficiency in handling structural biology data and understanding of biochemical principles.
- Strong programming skills and understanding of modern software development practices, especially in Python.
- Experience in building and deploying high-performance implementations of deep learning algorithms.
- Proven track record in machine learning, including 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.
- Passion for applying ML research to impactful problems.
- Authorship of publications in peer-reviewed conferences (e.g., NeurIPS, ICML, ICLR, or similar).
- Contribution to high-visibility ML codebases.
- Scientific knowledge of biology, chemistry, or physics along with previous experience working in a scientific environment across disciplines.
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