Iambic Therapeutics, Inc - Machine Learning Scientist — Large Multimodal Models (Post-Training)

• Research and develop post-training strategies for large-scale multimodal foundation models • Design reward functions, training objectives, data-generation strategies, and evaluation protocols for reinforcement learning and other post-training approaches applied to multimodal LLMs • Build systematic experimentation and hyperparameter optimization workflows to efficiently explore post-training recipes, model configurations, and training strategies • Develop and apply inference optimization techniques to support deployment in both high-throughput model evaluation and interactive discovery workflows • Design and maintain rigorous benchmarking and evaluation frameworks that measure model quality across modalities, downstream tasks, and scientific use cases • Collaborate with ML and software engineering colleagues to productionize models, evaluation systems, and inference services • Partner with computational chemists, medicinal chemists, and biologists to ensure model development and post-training objectives are grounded in drug discovery needs • Communicate results to internal teams, external partners, and at conferences • Write high-quality research and engineering code: refactor, test, document, and package ML components to support team velocity • PhD in machine learning, computer science, computational chemistry, physics, or a related computational STEM field, or equivalent industry experience demonstrating comparable depth • Strong Python and PyTorch skills, including implementing, training, debugging and evaluating deep learning models end-to-end • Demonstrated experience training large-scale transformer models • Demonstrated experience in one or more of the following: • Reinforcement learning approaches such as RLHF, RLAIF, PPO, GRPO, RL with verifiable rewards, or related methods (strongly preferred) • Supervised fine-tuning, full-parameter fine-tuning, parameter-efficient fine-tuning (LoRA), or related methods • Systematic hyperparameter optimization or large-scale experimentation using tools such as Optuna, Ray Tune, or similar frameworks • Strong engineering practices: reproducible experimentation, clean code, testing, and performance-aware debugging • Comfort with modern ML infrastructure (e.g., Docker, CUDA, Kubernetes, experiment tracking tools such as Weights & Biases) • Experience with multimodal or multi-task model architectures • Training and inference optimization (e.g., mixed precision, kernel optimization, quantization, distributed strategies) • Familiarity with biomedical, chemical, or biological data domains • Distributed training at scale • HPC or large-scale training operations experience • Iambic is a clinical-stage life-science and technology company developing novel medicines using its AI-driven discovery and development platform. Based in San Diego and founded in 2020, Iambic has assembled a world-class team that unites pioneering AI experts and experienced drug hunters. The Iambic platform has demonstrated delivery of new drug candidates to human clinical trials with unprecedented speed and across multiple target classes and mechanisms of action. Iambic is advancing a pipeline of potential best-in-class and first-in-class clinical assets, both internally and in partnership, to address urgent unmet patient need. Learn more about the Iambic team, platform, pipeline, and partnerships at iambic.ai. • MISSION & CORE VALUES • Our mission is to deliver better medicines through innovations in AI-based discovery technologies. The culture and work at Iambic Therapeutics are profoundly strengthened by the diversity of our people and our differences in background, culture, national origin, religion, sexual orientation, and life experiences. We are committed to building an inclusive environment where a diverse group of talented humans work together to discover therapeutics and create technologies.