Member of the Technical Staff, Pretraining

Responsibilities

• Advance the core architecture and training of Output's foundation model
• Design architectures and develop training objectives purpose-built for biological reasoning
• Develop methods for the model to learn across multiple biological data modalities simultaneously
• Extend model reasoning capabilities across binding, molecular properties, and biological function
• Own end-to-end pretraining including experiment design, distributed training on multi-GPU clusters, and hyperparameter optimization
• Design evaluation frameworks to measure real biological reasoning versus statistical patterns

Requirements

• PhD in CS, ML, Physics, Math, or related field with 2+ years post-doc/industry experience, OR Bachelor's/Master's with 5+ years hands-on research/engineering experience
• Strong publication record at top-tier venues (NeurIPS, ICML, ICLR) in pretraining, SSL, or foundation models
• Hands-on experience pretraining large models on diverse, heterogeneous data
• Proficiency in Python and PyTorch
• Experience training models on distributed multi-GPU infrastructure
• Ability to own the full research-to-training pipeline and write production-quality code
• Rigorous experimental approach to design evaluations and track experiments

Preferred Qualifications

• Background in chemistry, biology, computational biology, or biophysics
• Experience pretraining models on molecular or biological data
• Experience with multimodal learning or heterogeneous data sources
• Contributions to open-source machine learning projects

About the Company

Output is a stealth-mode startup building biological reasoning models to discover previously impossible drug treatments. Backed by Y Combinator and top-tier VCs, the team consists of repeat founders and biotech veterans.