Computational Chemist & AI Engineer

AI Scientist – Computational Chemistry & Machine Learning

A technology-driven company at the forefront of scientific innovation is seeking anAI Scientistwith expertise incomputational chemistryandapplied machine learningto help develop transformative tools for drug discovery and partner success.

Key Responsibilities

• Build and maintain strong relationships with external partners, delivering high-impact, transformational AI projects.
• Collaborate with multidisciplinary teams—including data scientists, software engineers, and product teams—to integrate emerging technologies into real-world solutions.
• Design and implementcutting-edge AI algorithms, ensuring their integration intorobust, production-grade platformsthat enhance research efficiency.
• Translate scientific and business goals intoscalable and maintainable softwaresolutions.
• Own thefull development lifecycle, from requirements gathering through to planning, coding, testing, and deployment.
• Stay current onadvancements in computational science and AI, applying relevant innovations to project work.

Core Qualifications

• MSc or PhD inComputational Chemistry,Cheminformatics,Quantum Mechanics, orAI for scientific discovery.
• Demonstrated impact in previousscientific or technical projects, ideally within the life sciences or drug discovery space.
• Advanced programming skills, especially inPython; experience in other languages (e.g.,C/C++,Java) is a plus.
• Strong communicator, able toclearly articulate scientific ideasto diverse technical and non-technical audiences.
• Collaborative, growth-oriented mindset with a passion forrapidly translating novel research into real-world applications.

Preferred Experience

Expertise in one or more of the following areas:

• Artificial Intelligence: Experience withGNNs,transformers,generative models,Gaussian processes, orreinforcement learning.
• Cheminformatics: Familiarity withchemical data formats,reaction prediction, and tools such asRDKitorOpenEye.
• Quantum Mechanics: Practical use ofQM methodsfor synthesis prediction using tools likePSI4,Orca, orGaussian.
• Big Data: Experience curating and processing data from diverse sources; exposure toApache SparkorHadoopis beneficial.
• Cloud Platforms: Proficiency withAWS,GCP, orAzure.
• ML Frameworks: Hands-on withscikit-learn,TensorFlow,PyTorch, or related libraries.