Member of the Technical Staff, Generative

About the role

Output is revolutionizing drug discovery through its advanced biological reasoning model, capable of generating novel therapeutic molecules previously inaccessible to traditional methods. This role offers a unique opportunity to contribute to groundbreaking research and development.

Responsibilities

• Design and develop generative architectures for molecular data across various modalities, including small molecules, peptides, and mini proteins.
• Develop training approaches that leverage diverse biological signals to ensure the model produces genuinely novel structures.
• Create methods for controllable, targeted generation, allowing the model to produce molecules with specified biological properties while adhering to real-world chemical constraints.
• Integrate biological reasoning from the foundation model into the generative pipeline, guiding and conditioning generation processes with learned biological representations.
• Own the entire research-to-training pipeline, from experiment design to distributed training on multi-GPU clusters, hyperparameter optimization, and iterative refinement.
• Design and implement evaluation frameworks that assess the biological significance, structural validity, and novelty of generated molecules.

Requirements

• 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 generative modeling.
• A strong publication record in generative methods at top-tier venues such as NeurIPS, ICML, and ICLR.
• Extensive hands-on experience designing, building, and training deep generative models, including work on novel architectures, training objectives, or sampling methods.
• Proficiency in Python and PyTorch, with experience training models on distributed multi-GPU infrastructure.
• Demonstrated ability to own the full research-to-training pipeline, from method design to model deployment.
• Production-quality code that is well-tested, maintainable, and integrated into shared codebases with version control and code review practices.
• A rigorous experimentalist who designs evaluations carefully, tracks experiments systematically, and draws conclusions from data.

Qualifications

• Experience applying generative models to molecular, chemical, or biological data.
• A background in chemistry, biology, computational biology, biophysics, or a related natural science.
• Experience with multi-modal learning or cross-modality translation.
• Experience with conditional or controllable generation methods.
• Contributions to open-source machine learning projects.

Skills

• Generative modeling
• Deep learning
• Molecular data analysis
• Chemical and biological data processing
• Model training and optimization
• Evaluation and validation of generative models

Benefits

• Healthcare benefits including medical, dental, and vision coverage.
• Competitive salary and equity in a rapidly growing startup.
• Flexible work arrangements.
• Continuous professional development opportunities.
• Work-life balance with a supportive and inclusive culture.

Pay

$150K - $350K

Schedule

Full-time

Company Overview

Output is a stealth-mode company led by a team of repeat founders and biotech veterans with multiple exits in AI x Bio. Backed by top-tier VCs including Y Combinator, Output is dedicated to solving some of the hardest problems in AI and biology, with a focus on developing innovative solutions for drug discovery and personalized medicine.