Advisor - AI/ML Generative Foundation Models for Biotherapeutics

• Advisor - AI/ML Generative Foundation Models for Biotherapeutics
• Help us push the de novo protein design frontier beyond binders to molecules that can become medicines, faster. Ready to make an impact? Join us.
• Develop model architectures , implementing, adapting, and where needed inventing approaches for protein design across sequence-structure generation, epitope conditioning, and confidence prediction.
• Contribute novel technical ideas and research directions , helping advance the state of the art in foundation models and generative AI for biotherapeutics.
• Run training at scale , executing training and fine-tuning experiments on LillyPod while tracking configurations, checkpoints, and results for reproducibility.
• Curate training data , preparing internal structure, sequence, and assay data, including processing and interpretation, in partnership with subject matter experts.
• Evaluate and calibrate , building evaluations that connect model outputs to experimental results.
• Diagnose model failures , investigating failure modes, identifying root causes, and developing improvements to model architectures, data strategies, or training approaches.
• Partner cross-functionally , working closely with peers, protein scientists, and automation colleagues to benchmark model performance.
• Communicate results , presenting clearly to both computational and wet-lab audiences and publishing at external conferences and in the scientific community.
• Fluency in Python and PyTorch, with hands-on experience training deep learning models on GPUs, including multi-node or distributed runs.
• Peer-reviewed publication or equivalent public contribution applying machine learning to molecular or biological data.
• Ability to develop and implement new deep learning methods.
• Consistent record of clean, tested, reproducible research code and version-controlled, shared training pipelines.
• Participation in the open-source community through released code, models, benchmarks, or other widely used contributions.
• Experience working with wet-lab data, including noisy and negative experimental results, and partnering with the scientists designing those experiments.
• Experience mentoring interns or junior scientists, or leading a small technical sub-project.
• Proficiency with antibody or VHH molecules is desired but not required.
• Strong written and verbal communication skills with scientists of diverse expertise and backgrounds.
• Ability to work productively in an interdisciplinary environment spanning computational and experimental sciences.
• Actual compensation will depend on a candidate’s education, experience, skills, and geographic location.  The anticipated wage for this position is
• $168,000 - $268,400

None flagged.