Machine Learning Scientist — Large Multimodal Models (Post-Training)

Summary

Iambic Therapeutics is a clinical-stage life-science and technology company developing medicines through AI-driven discovery and development technologies. The Machine Learning Scientist will research and develop post-training methods for large multimodal foundation models, build evaluation and experimentation infrastructure, optimize inference, and collaborate with machine learning, engineering, and drug discovery teams to deploy models for therapeutic decision-making.

Responsibilities

• 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

Skills

• 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:
• 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)
• Reinforcement learning approaches such as RLHF, RLAIF, PPO, GRPO, RL with verifiable rewards, or related methods (strongly preferred)
• 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

Qualifications

Must Haves

• 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:
• 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)

Nice to Haves

• Reinforcement learning approaches such as RLHF, RLAIF, PPO, GRPO, RL with verifiable rewards, or related methods (strongly preferred)
• 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

Benefits

• Company-paid healthcare
• Flexible spending accounts
• Voluntary life insurance
• 401(k) matching
• Uncapped vacation
• Remote position, with the option to be on-site in the Boston office
• Onsite gym
• Dining