AI Scientist – Computational Chemistry & Machine Learning
A technology-driven company at the forefront of scientific innovation is seeking an AI Scientist with expertise in computational chemistry and applied machine learning to 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 implement cutting-edge AI algorithms, ensuring their integration into robust, production-grade platforms that enhance research efficiency.
* Translate scientific and business goals into scalable and maintainable software solutions.
* Own the full development lifecycle, from requirements gathering through to planning, coding, testing, and deployment.
* Stay current on advancements in computational science and AI, applying relevant innovations to project work.
Core Qualifications
* MSc or PhD in Computational Chemistry, Cheminformatics, Quantum Mechanics, or AI for scientific discovery.
* Demonstrated impact in previous scientific or technical projects, ideally within the life sciences or drug discovery space.
* Advanced programming skills, especially in Python; experience in other languages (e.g., C/C++, Java) is a plus.
* Strong communicator, able to clearly articulate scientific ideas to diverse technical and non-technical audiences.
* Collaborative, growth-oriented mindset with a passion for rapidly translating novel research into real-world applications.
Preferred Experience
Expertise in one or more of the following areas:
* Artificial Intelligence: Experience with GNNs, transformers, generative models, Gaussian processes, or reinforcement learning.
* Cheminformatics: Familiarity with chemical data formats, reaction prediction, and tools such as RDKit or OpenEye.
* Quantum Mechanics: Practical use of QM methods for synthesis prediction using tools like PSI4, Orca, or Gaussian.
* Big Data: Experience curating and processing data from diverse sources; exposure to Apache Spark or Hadoop is beneficial.
* Cloud Platforms: Proficiency with AWS, GCP, or Azure.
* ML Frameworks: Hands-on with scikit-learn, TensorFlow, PyTorch, or related libraries.