You will advance the core architecture and training of Output's foundation model, the system that learns biological reasoning from data. This role spans the full arc from research to trained model: you design architectures, develop training objectives, run pretraining at scale, and evaluate what the model has learned. - You will push forward the architecture and training objectives of our foundation model, designing approaches that are purpose-built for biological reasoning - You will develop methods for the model to learn across multiple biological data modalities simultaneously, building unified representations of molecular biology - You will extend the model's reasoning capabilities across biological phenomena, pushing what it can predict and understand about binding, molecular properties, and biological function - You will own pretraining end-to-end: experiment design, distributed training on multi-GPU clusters, hyperparameter optimization, and iteration - You will design evaluation frameworks that measure whether the model has learned real biological reasoning, not just statistical patterns in training data About You - You have a PhD in computer science, machine learning, physics, mathematics, or a related field with 2+ years of post-doctoral or industry research experience, or a Bachelor's or Master's degree with 5+ years of hands-on research and engineering experience in representation learning and model pretraining - You have a strong publication record at top-tier venues (e.g., NeurIPS, ICML, ICLR) with contributions to pretraining methods, self-supervised learning, representation learning, or foundation models - You have hands-on experience pretraining large models on diverse, heterogeneous data, including designing training objectives and scaling training infrastructure - You are proficient in Python and PyTorch, and have experience training models on distributed multi-GPU infrastructure - You have demonstrated the ability to own the full research-to-training pipeline: you do not just design methods, you train and ship models - You write production-quality code that is well-tested and maintainable, and you are comfortable working in shared codebases with version control and code review - You are a rigorous experimentalist who designs evaluations carefully, tracks experiments systematically, and draws conclusions from data rather than intuition Bonus Points - You have a background in chemistry, biology, computational biology, biophysics, or a related natural science - You have experience pretraining models on molecular or biological data - You have experience with multimodal learning or learning from heterogeneous data sources - You have contributed to open-source machine learning projects What We Offer - We encourage new and different ideas, creativity and contrarian thinking - Healthy feedback focused environment to help you strive - leadership will have high expectations, regularly share constructive feedback, support you and help you grow, and welcome receiving feedback and ideas from you - You own your day-to-day management. What we care about is that we all hit our milestones - Competitive salary and equity in a growing, well-funded startup - Excellent medical, dental, and vision coverage