NYC Staffing
Senior Machine Learning Scientist, AI for Drug Discovery (Structure, Scoring, an
NYC Staffing, New York, New York, United States, 10001
Machine Learning Scientist Opportunity
A healthier future. It's what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That's what makes us Roche. Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche's Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. The Opportunity: We are looking for talented Machine Learning Scientists to join Prescient Design, a division devoted to developing structural and machine learning based methods for molecular design within the Roche organization. The successful candidate will design, develop, and deploy new techniques for machine learning based molecular optimization for the analysis and design of small and large molecule drugs within target-driven design campaigns. Candidates may work with molecular simulation, property prediction based on sequence and/or 2D/3D structure, guided generation, cofolding, ranking, target/pocket/epitope assessment, or de novo design. In this role, you will: Join Prescient Design within the Computational Sciences organization in gRED. Your peers will be machine learning scientists, engineers, computational chemists, and computational biologists. Closely collaborate with scientists within Prescient and across gRED. Develop machine learning and computational workflows to analyze existing, and design new, small and large molecules. Form close working relationships with small molecule and protein therapeutic development efforts across the gRED organization. Work on existing projects and generate new project ideas. Who you are: PhD degree in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, and/or the like), or MS degree and 3+ years of industry experience. Demonstrated experience with machine learning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases). Record of achievement, including at least one high-impact first author publication or equivalent. Excellent written, visual, and oral communication and collaboration skills. Additional desired skills include experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit). Previous focus on one or more of these areas: molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, statistical methods. Public portfolio of computational projects (available on e.g. GitHub). This opportunity needs to be based in South San Francisco, New York City, or Basel. Relocation benefits are available for this job posting. The expected salary range based on the location of California, is $167,400 - $310,800. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
A healthier future. It's what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That's what makes us Roche. Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche's Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. The Opportunity: We are looking for talented Machine Learning Scientists to join Prescient Design, a division devoted to developing structural and machine learning based methods for molecular design within the Roche organization. The successful candidate will design, develop, and deploy new techniques for machine learning based molecular optimization for the analysis and design of small and large molecule drugs within target-driven design campaigns. Candidates may work with molecular simulation, property prediction based on sequence and/or 2D/3D structure, guided generation, cofolding, ranking, target/pocket/epitope assessment, or de novo design. In this role, you will: Join Prescient Design within the Computational Sciences organization in gRED. Your peers will be machine learning scientists, engineers, computational chemists, and computational biologists. Closely collaborate with scientists within Prescient and across gRED. Develop machine learning and computational workflows to analyze existing, and design new, small and large molecules. Form close working relationships with small molecule and protein therapeutic development efforts across the gRED organization. Work on existing projects and generate new project ideas. Who you are: PhD degree in a quantitative field (e.g., Computer Science, Chemistry, Chemical Engineering, Computational Biology, Physics, and/or the like), or MS degree and 3+ years of industry experience. Demonstrated experience with machine learning libraries in production-ready workflows (e.g., PyTorch + Lightning + Weights and Biases). Record of achievement, including at least one high-impact first author publication or equivalent. Excellent written, visual, and oral communication and collaboration skills. Additional desired skills include experience with physical modeling methods (e.g., molecular dynamics) and cheminformatics toolkits (e.g., rdkit). Previous focus on one or more of these areas: molecular property prediction, computational chemistry, de novo drug design, medicinal chemistry, small molecule design, self-supervised learning, geometric deep learning, Bayesian optimization, probabilistic modeling, statistical methods. Public portfolio of computational projects (available on e.g. GitHub). This opportunity needs to be based in South San Francisco, New York City, or Basel. Relocation benefits are available for this job posting. The expected salary range based on the location of California, is $167,400 - $310,800. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.