Machine Learning Scientist — Large multimodal models

Iambic Therapeutics is a clinical-stage life-science and technology company focused on developing novel medicines through AI-driven discovery technologies. The Machine Learning Scientist will research, develop, and scale multimodal transformer models for drug discovery, collaborating with various teams to implement and optimize these models for real-world applications.

Responsibilities

• Research and implement architectural improvements to large-scale multimodal transformer models for biomedical applications
• Investigate hybrid modeling approaches that combine learned representations with domain-informed structure or inductive biases
• Optimize training pipelines for efficiency, stability, and scalability across many-GPU clusters
• Develop and apply inference optimization techniques to support deployment in interactive discovery workflows
• Design and maintain benchmarking and evaluation frameworks that track model quality across modalities and downstream tasks
• Collaborate with ML and software engineering colleagues to deploy and operationalize models
• Partner with computational chemists, medicinal chemists, and biologists to ensure model development is 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
• Depending on level: Mentor interns and junior team members through technical guidance, code reviews, and best practices in ML experimentation
• Contribute to the strategic research roadmap for Enchant and related multimodal technologies

Skills

• MS in ML/CS or a computational STEM field with relevant industry or research experience, or PhD or equivalent industry experience demonstrating comparable depth
• Strong Python and PyTorch experience, including implementing and training deep learning models end-to-end
• Demonstrated experience training transformer models at scale
• Strong engineering habits: reproducible experimentation, clean code, testing, and performance-minded debugging
• Comfort working with modern ML infrastructure (e.g., Docker, CUDA, Kubernetes, experiment tracking 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

Benefits

• Industry leading competitive pay
• Company paid healthcare
• Flexible spending accounts
• Voluntary life insurance
• 401K matching
• Uncapped vacation
• Brand-new state-of-the art facility in beautiful San Diego with an onsite gym, dining, and easy access to great places to live and play

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