Staff Research Scientist, Computational Toxicology

Summary

Join SandboxAQ, a high-growth company developing AI solutions for global challenges, as a Staff Computational Biologist/Toxicologist. You will be part of the Large Quantitative Model (LQM) team, developing novel computational tools to revolutionize drug discovery. This role requires expertise in computational biology, toxicology, and AI/ML techniques, particularly deep learning. You will leverage your skills to predict ADME/tox properties, lead technology creation, collaborate with cross-functional teams, and contribute to publications and patents. The position offers a competitive salary, benefits, and opportunities for professional growth within a dynamic and innovative environment.

Requirements

• Ph.D. in computational biology, bioinformatics, computer science, or related data science fields with 4+ years of biopharma industry experience
• Experience with computational methods for ADME/tox and pK/pD prediction, ideally including knowledge graph methods
• Experience with contemporary AI/ML techniques, including deep learning architectures, and ideally including GNNs
• Extensive hands on experience with high-throughput sequencing data, such as RNA-seq, single-cell RNA-seq data, genomic (whole-genome and exome) data, and/or proteomic data, including retrieval of these from public repositories
• Experience leading technical projects
• Advanced proficiency with Python, R, including for ML (i.e. with PyTorch or TensorFlow) and database management
• Excellent communication skills

Responsibilities

• Leverage expertise in computational biology and toxicology to predict ADME/tox properties of new drugs
• Help lead the creation of next-generation technology to do the above
• Work with a cross-functional team of experts to computerize drug discovery
• Apply deep learning approaches to the above. Drive curation of high-quality datasets
• Write patents, research papers and technical documents. Participate and present at international conferences

Preferred Qualifications

• Relevant postdoctoral training
• Experience with physics-based simulation e.g. for pK/pD modeling
• Experience in long-context sequence modeling
• Direct experience in drug discovery or development
• Experience with or knowledge of regulatory drug safety evaluations

Benefits

• Annual discretionary bonuses
• Equity
• 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