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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Scientist - **Company:** Ten63 Therapeutics, Inc. - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Microsoft Azure, Computer Clusters, Python (Programming Language), Machine Learning, Molecular Modeling, Monte Carlo Methods, Cisco Nexus Switches, Tensorflow, Scientific Computating, Software Deployment, Software Engineering, Software Systems, Reinforcement Learning, Data Processing, Graphics Processing Unit (GPU), Google Cloud, Enterprise Software Applications, Cloud Platform System, Pytorch, Uncertainty Quantification (UQ), Large Language Models, Deep Learning, Generative AI, Gaussian, AutoML, Transformer Architectures, Machine Learning Operations, Feature Extraction, Recurrent Neural Networks - **Published:** October 12, 2026 - **Apply:** https://www.thejobnetwork.com/job/70b258a7-aaf3-4645-bef7-2ee7e1415105/senior-ml-scientist ## About the Role * PhD in Computational Chemistry, Chemical Engineering, Chemistry, Computer Science with chemistry focus, or related field, or equivalent industry experience \n * 4+ years of professional experience developing and deploying machine learning models in production environments \n * Expert-level Python programming with demonstrated ability to build complex scientific software systems \n * Expertise in deep learning and experience designing and training multiple types of neural network architectures (CNNs, RNNs, Transformers, Diffusion models, GANs, etc.) Please note that a high degree of depth in any single type of architectures is required, and having familiarity with every single architecture listed is not strictly necessary. \n * Proven experience with large-scale model training on GPU clusters, including projects requiring weeks to months of continuous training time \n * Experience with distributed training across multiple GPUs and optimization of training pipelines for computational efficiency \n * Strong computational chemistry background with hands-on experience running and analyzing physics-based simulations such as molecular dynamics, Monte Carlo methods, or quantum chemical calculations. As with the deep learning architectures, a high degree of familiarity with at least one of these types of simulations is required. \n * Deep understanding of cheminformatics principles and practical experience with molecular data processing, including molecular file formats, molecular descriptors, fingerprints, and substructure searching \n * Proficiency with modern deep learning frameworks (PyTorch, TensorFlow, JAX) \n * Experience with molecular representation learning and applying ML to chemical/biological problems \n * Strong knowledge of 3D molecular structures, protein-ligand interactions, and structure-based drug design principles \n * Proven track record of delivering complex ML projects from research through production deployment \n * Excellent communication skills with demonstrated ability to effectively collaborate with chemists, engineers, and drug designers \n * Ability to translate scientific requirements into technical specifications \n * Passion for solving seemingly impossible problems and pushing the boundaries of what's possible \n * Strong problem-solving skills and ability to work at the intersection of chemistry, physics, and machine learning \n, * Experience with reinforcement learning for molecular design or optimization \n * Specific expertise with any of the following: transformer architectures, large language models, or diffusion models \n * Experience with generative AI for chemistry and molecular design \n * Knowledge of equivariant neural networks or geometric deep learning for molecular systems \n * Experience analyzing molecular dynamics trajectories and identifying key collective variables \n * Knowledge of uncertainty quantification in ML models for chemistry \n * Background in quantum chemistry calculations and their integration with ML workflows \n * Experience with computational chemistry software packages (Gromacs, AMBER, OpenMM, PSI4, ORCA, Gaussian, etc.) \n * Hands-on experience with cheminformatics libraries (RDKit, Open Babel, CDK) and molecular property prediction tools \n * Experience with protein-ligand docking, virtual screening, or ADMET prediction \n * Experience with automated machine learning (AutoML) or hyperparameter optimization for molecular models \n * Publications in computational chemistry, machine learning for drug discovery, or AI/ML conferences \n * Familiarity with experiment tracking and model management tools (MLflow, Weights & Biases, etc.) \n * Experience with cloud computing platforms (AWS, GCP, Azure) for large-scale ML training ## Description We are seeking an exceptional Senior ML Scientist to join our team of pioneering scientists, engineers, drug hunters and quantum chemists. This role requires a unique combination of deep expertise in state-of-the-art deep learning techniques and physics-based molecular simulations to develop the AI models powering Ten63's BEYOND platform. You will design and train sophisticated deep learning architectures and lead model development for our computational chemistry workflows, and work with our AI engineers to promote validated models into production software environments. As a key technical liaison, you will sit at the nexus of computational chemistry, software engineering, and drug development, translating complex scientific requirements into robust AI-driven solutions.