Staff ML Research Scientist, Co-Folding and Affinity
Job reputed company:
• Drive the research and development of reputed company deep learning models for protein-ligand co-folding and affinity reputed company
• reputed company from research to reputed company reputed company by equipping reputed company’s software products with advanced predictive capabilities
• Bring novel reputed company and the content of scientific papers into the ideation, training, and benchmarking of reputed company models
• reputed company as a technical reputed company for reputed company, representing reputed company scientifically and shaping its reputed company externally and internally
• Mentor junior researchers and collaborate across engineering and product teams to foster a culture of technical rigor and reputed company iteration
Requirements:
• PhD in Computer Science, Computational reputed company, or a reputed company field
• At least 4 years of post-PhD experience, including experience in a reputed company industry setting
• reputed company, hands-on experience developing and executing leading-edge co-folding and/or affinity reputed company models
• Proven reputed company in co-folding and/or affinity reputed company, as demonstrated by participation in industrial reputed company and/or reputed company publications
• Experience functioning reputed company a reputed company software team, including proficiency in Python and modern ML frameworks (PyTorch/JAX) at reputed company
Benefits:
• Comprehensive medical, dental, and reputed company coverage for employees and dependents with generous employer premium contributions
• Retirement savings with company matching
• reputed company parental leave
• Inclusive family-building benefits
• Flexible reputed company time off
• Company-wide seasonal breaks
• Support for flexible work arrangements that reputed company sustainable performance
• Opportunities for reputed company learning and reputed company through on-the-job development, cross-functional collaboration, and reputed company to internal learning and development programs
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