Senior ML Scientist (Durham)

About Ten63 Therapeutics

Ten63 Therapeutics is focused on improving human health by developing better, more durable therapeutics against cancer and some of the world's most lethal diseases. We are leveraging our proprietary in silico platform BEYOND to generate novel therapeutics to hit previously undruggable targets. We're pioneering an engineered approach to drug discovery by scaling quantum simulations and fusing them with generative chemistry to tackle previously undruggable, high-impact targets. Our team of pioneering scientists, engineers, drug hunters, and quantum chemists values passion, teamwork, out-of-the-box thinking and determination to solve even what seems impossible.

Position Overview

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.

Key Responsibilities

• Design, implement, and train deep learning models for a variety of drug-design applications, using a variety of cutting-edge architectures, including transformers, diffusion networks, GANs, flow networks, etc. (Note that equal levels of familiarity with all the architectures mentioned above is not expected.)
• Lead large-scale model training efforts on multi-GPU compute clusters, including experiments that span weeks to months
• Design methods that couple physics-based simulation (molecular dynamics, quantum calculations, Monte Carlo methods) with learned models
• Prototype and develop methods for using the models you build in structure-based drug design
• Design the datasets, splits, and benchmarks that models are trained and evaluted on — which data, which labels, and which held-out sets reflect real prospective use across protein structures, small molecules, molecular descriptors, and simulation trajectories
• Design validation that reflects prospective use, including scaffold and temporal splits, applicability domain, and calibrated uncertainty, and be candid about where a model should not be trusted
• Partner with drug designers to turn target-specific questions into tractable ML problems, driving innovation in AI/ML approaches to undruggable targets and data-poor target spaces
• Mentor scientists and contribute to the scientific direction and technical strategy for our in silico platform and AI capabilities
• Uphold reproducibility and experimental rigor through experiment tracking, model versioning, code review, testing, and documentation
• Stay current with the literature and publish or present work where appropriate

Required Qualifications

• PhD in Computational Chemistry, Chemical Engineering, Chemistry, Computer Science with chemistry focus, or related field, or equivalent industry experience
• 4+ years of professional experience developing and deploying machine learning models in production environments
• Expert-level Python programming with demonstrated ability to build complex scientific software systems
• 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.
• Proven experience with large-scale model training on GPU clusters, including projects requiring weeks to months of continuous training time
• Experience with distributed training across multiple GPUs and optimization of training pipelines for computational efficiency
• 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.
• Deep understanding of cheminformatics principles and practical experience with molecular data processing, including molecular file formats, molecular descriptors, fingerprints, and substructure searching
• Proficiency with modern deep learning frameworks (PyTorch, TensorFlow, JAX)
• Experience with molecular representation learning and applying ML to chemical/biological problems
• Strong knowledge of 3D molecular structures, protein-ligand interactions, and structure-based drug design principles
• Proven track record of delivering complex ML projects from research through production deployment
• Excellent communication skills with demonstrated ability to effectively collaborate with chemists, engineers, and drug designers
• Ability to translate scientific requirements into technical specifications
• Passion for solving seemingly impossible problems and pushing the boundaries of what's possible
• Strong problem-solving skills and ability to work at the intersection of chemistry, physics, and machine learning

Preferred Qualifications

• Experience with reinforcement learning for molecular design or optimization
• Specific expertise with any of the following: transformer architectures, large language models, or diffusion models
• Experience with generative AI for chemistry and molecular design
• Knowledge of equivariant neural networks or geometric deep learning for molecular systems
• Experience analyzing molecular dynamics trajectories and identifying key collective variables
• Knowledge of uncertainty quantification in ML models for chemistry
• Background in quantum chemistry calculations and their integration with ML workflows
• Experience with computational chemistry software packages (Gromacs, AMBER, OpenMM, PSI4, ORCA, Gaussian, etc.)
• Hands-on experience with cheminformatics libraries (RDKit, Open Babel, CDK) and molecular property prediction tools
• Experience with protein-ligand docking, virtual screening, or ADMET prediction
• Experience with automated machine learning (AutoML) or hyperparameter optimization for molecular models
• Publications in computational chemistry, machine learning for drug discovery, or AI/ML conferences
• Familiarity with experiment tracking and model management tools (MLflow, Weights & Biases, etc.)
• Experience with cloud computing platforms (AWS, GCP, Azure) for large-scale ML training

Technical Skills

• Python for scientific computing
• Deep learning frameworks (PyTorch, TensorFlow, JAX)
• Large-scale and multi-GPU model training
• Cheminformatics libraries (RDKit, Open Babel)
• Molecular simulation packages (Gromacs, AMBER, OpenMM, or similar)
• High-performance computing and SLURM or similar job schedulers
• Scientific computing libraries (NumPy, SciPy, scikit-learn, pandas)
• Version control systems (Git)
• Experiment tracking and model management tools
• Molecular visualization tools (PyMOL, VMD, or similar)
• Dataset curation and preparation for large-scale molecular data
• Linux/Unix environments and bash scripting

What We Offer

• Opportunity to work on previously undruggable targets and fundamentally change the paradigm of drug discovery
• Collaborative environment with world-class scientists, engineers, drug hunters, and quantum chemists
• Competitive compensation package including equity and an annual bonus
• Comprehensive health, life, short- and long-term disability insurance policies
• 401K matching
• Access to cutting-edge computational resources and our proprietary BEYOND platform
• 24/7 access to a world-class fitness center
• Professional development and mentoring opportunities

Ten63 Therapeutics is an equal opportunity employer. We embrace diversity and are committed towards inclusion. We encourage applications from all qualified candidates regardless of race, color, religion, sex, national origin, age, disability, or any other protected characteristic.