Member of the Technical Staff, Pretraining

Output has built a biological reasoning model that understands biology at the scale and complexity life actually operates. Our model independently learned the principles of molecular interactions, opening up drug treatments that were previously impossible. We're already generating therapies that traditional approaches cannot reach. The hardest problems in both AI and biology are being solved here, and there is room for you to own one. Output is currently in stealth, operated by a team of repeat founders and biotech veterans with multiple exits in AI x Bio, and backed by top-tier VCs including Y Combinator. 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