Remote Senior Research Scientist

Summary

Join SandboxAQ to contribute your structure-based drug design expertise and drive cutting-edge research at the intersection of machine learning, biophysics, and quantum physics.

Requirements

• PhD (or equivalent) in computer science, computational chemistry, computational physics, or any related field with a focus on machine learning
• 3-5 years (including PhD) of relevant research experience
• Proven experience applying ML to structure-based drug design as evidenced e.g. by co-authorship of research publications
• Proven track record of developing ML algorithms, in particular, deep learning approaches to structure prediction, force field development, and similar
• Strong communication skills and experience presenting scientific results to less educated audiences
• Excellent knowledge of modern DL architectures such as diffusion models, transformer models, flow-based networks, variational autoencoders, and similar
• Proficiency in programming languages and frameworks commonly used in machine learning and data analysis, such as Python or Julia, with relevant libraries and frameworks (e.g. jax, TensorFlow, PyTorch, scikit-learn, or others)
• Experience with software development in a dynamic startup environment, i.e. fluency with git, GitHub, cloud computing, coding best practices, code review a.s.o

Responsibilities

• Research, develop, and implement modern deep learning approaches to relevant structure-based drug design problems in biopharma and drug discovery
• Development and implementation of generative AI frameworks (diffusion models, transformers, autoencoders aso) into functioning PoCs
• Development, implementation, and extension of ML methods for structure prediction such as AlphaFold, openFold, RosettaFold, uMol, or similar
• Contribution to the development of machine-learned force fields for classical simulation of molecular systems with applications in biopharma, material science, and more
• Develop and implement novel machine learning algorithms and computational models to analyze diverse biomedical datasets, including genomics, proteomics, ADME-tox, and more
• Evaluate the performance of machine learning models through rigorous validation and benchmarking against known data
• Collaborate with cross-functional teams of medicinal chemists, computational chemists, physicists, and engineers in the area of machine learning for drug discovery, chemistry, and material science
• Support of client engagements

Benefits

• Competitive salaries
• Stock options depending on employment type
• Generous learning opportunities
• Medical/dental/vision
• Family planning/fertility
• PTO (summer and winter breaks)
• Financial wellness resources
• 401(k) plans