Machine Learning Cheminformatics Engineer, Drug Discovery (EMEA) SandboxAQs AI Simulation group partners with global research teams to discover new drugs and materials using AI and physics-based computational solutions. We are seeking an experienced researcher to drive innovative and impactful projects leveraging cheminformatics, machine learning, and computational chemistry for drug discovery. The successful candidate will demonstrate strong abilities in cheminformatics and/or bioinformatics, including knowledge of established techniques and cutting-edge machine learning methods for modeling molecular properties and interactions with complex systems. They will also have experience with scientific programming and data science. These skills will be leveraged within a seasoned, agile, and multi-disciplinary group, including drug hunters with an excellent track record in drug discovery, computational chemists, physicists, AI experts, and software engineers.

A variety of soft skills and experience may be required for the following role Please ensure you check the overview below carefully. What Youll Do Design and implement software that leverages informatics, machine learning, and computational chemistry to address unmet needs in drug discovery Contribute to ongoing research leveraging physics-based simulation, deep learning, and knowledge graphs for drug discovery applications Work closely with an interdisciplinary team of scientists to identify hits and optimize leads in ongoing drug discovery programs Leverage Bayesian optimization and active learning to improve experimental designs and make data-driven decisions Collaborate with computational chemistry experts and cross-functional teams to rapidly prototype and scale cutting-edge, impactful drug design solutions. Translate research and applications to maintainable software systems Contribute to the scientific community by writing patents / journal articles and presenting at conferences Translate insights from statistics, multimodal data analysis, and ML to actionable and testable drug discovery hypothesis This is an opportunity to directly contribute to the discovery of novel innovative medicines by applying computational chemistry techniques on teams with experienced multidisciplinary drug hunters About You PhD in chemistry, biology, computer science, or a related discipline 1-5 years of relevant experience including hands-on experience with informatics, machine learning, and computational chemistry applied to drug discovery in the private sector, like biotech or pharma Experience with cheminformatics and bioinformatics methods (e.g., similarity / substructure searching, reaction-based enumeration, sequence alignment, etc.) Experience with molecular property prediction and multi-objective optimization using machine learning and / or deep learning methods Experienced with common python toolkits for scientific computing (e.g., numpy, pandas, scipy), machine learning (e.g., scikit-learn, pytorch), and cheminformatics / bioinformatics (e.g., rdkit, openeye, biotite, biopython) Familiarity running simulations and training models on high-performance computing (GPU) environments for corporate R&D, innovation labs, or academic research An interest in solving scientific problems in chemistry and biology via computational and data-driven methods A drive to cooperate with colleagues to identify problems and communicate technical solutions in an accessible manner Hands-on mentality & comfortable with getting deep into the technical weeds of highly complex problems, and a track record of driving projects to completion The US base salary range for this full-time position is expected to be $142k $198k per year. Our salary ranges are determined by role and level. Within the range, individual pay is determined by factors including job-related skills, experience, and relevant education or training. This role may be eligible for annual discretionary bonuses and equity.

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