Senior ML Scientist

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. We embrace diversity and are committed towards inclusion.

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 computational chemistry, physics-based molecular simulations, and state-of-the-art deep learning to build the computational infrastructure and AI models powering our BEYOND platform. You will design and train sophisticated deep learning architectures and lead the development of large-scale machine learning pipelines that integrate seamlessly with our computational chemistry workflows. As a key technical liaison, you will bridge the gap between chemists, machine learning engineers, and drug designers, 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 of the architectures mentioned above is not expected.)
• Lead large-scale model training efforts on multi-GPU compute clusters, managing training pipelines that span weeks to months
• Develop and optimize machine learning workflows that integrate physics-based simulations (molecular dynamics, quantum calculations, Monte Carlo methods) with AI models
• Build high-performance Python software for cheminformatics, molecular property prediction, and structure-based drug design
• Collaborate with research scientists and quantum chemists to translate complex computational chemistry methodologies into scalable ML pipelines
• Implement and optimize distributed training strategies for large-scale models across GPU clusters
• Design data processing pipelines for molecular datasets, including handling of protein structures, small molecules, molecular descriptors, and simulation trajectories
• Serve as a technical bridge between computational chemists, ML engineers, drug designers, and medicinal chemists, effectively communicating complex technical concepts across disciplines
• Drive innovation in AI/ML approaches to undruggable targets and data-poor target spaces
• Mentor engineers and contribute to technical strategy for our in silico platform and AI capabilities
• Implement best practices in ML engineering including experiment tracking, model versioning, code review, testing, and documentation
• Facilitate cross-functional collaboration through clear, effective communication of technical requirements and solutions

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
• Proficiency with GPU programming (CUDA) and high-performance computing environments
• 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 automated machine learning (AutoML) or hyperparameter optimization for molecular models
• Background in quantum chemistry calculations and their integration with ML workflows
• Publications in computational chemistry, machine learning for drug discovery, or AI/ML conferences
• Experience with protein-ligand docking, virtual screening, or ADMET prediction
• Familiarity with MLOps tools and practices (MLflow, Weights & Biases, Kubeflow, etc.)
• Experience with cloud computing platforms (AWS, GCP, Azure) for large-scale ML training
• Knowledge of uncertainty quantification in ML models for chemistry
• C programming skills for performance-critical code components

Technical Skills

• Expert-level Python programming and software engineering
• Deep learning frameworks (PyTorch, TensorFlow, JAX)
• Distributed training and multi-GPU computing
• Cheminformatics libraries (RDKit, Open Babel)
• Molecular simulation packages (Gromacs, AMBER, OpenMM, or similar)
• GPU programming and optimization
• 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)
• Data processing pipelines for large-scale datasets
• 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
• Professional development 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.