Computation Scientist, Protein Design

Ubiqui Tx builds genetically encoded therapeutics that edit the proteome. Our core platform fuses short, designed peptide binders to post-translational modification enzymes (E3 ligases, deubiquitinases, kinases, phosphatases) to selectively modulate disease‑driving proteins in cells. All binders are designed in-house using our own generative models and validated experimentally. We are hiring a computational scientist to work directly on model execution, data generation, and learning systems that close the loop between experiments and model updates.

• Run and adapt our existing peptide and protein generative models for new targets.
• Design and launch binder libraries using our internal workflows.
• Manage inference, sampling, filtering, and ranking of candidates.
• Work closely with experimentalists to translate design outputs into testable constructs.
• Debug model behavior, sampling pathologies, and failure modes in real design campaigns.

• Systems to learn from experimental outcomes and feed that data back into model training.
• Data pipelines that connect binding, degradation, or PTM readouts to model weights.
• Training and fine‑tuning strategies that exploit sparse, noisy biological data.
• Evaluation and tracking tools to understand when and why models succeed or fail.
• Infrastructure that supports iterative design rather than one‑off runs.

• Strong Python and PyTorch experience.
• Hands‑on experience running large neural models, not just prototyping them.
• Familiarity with protein or peptide modeling, protein language models, or generative models.
• Comfort working with imperfect data and tight experimental feedback loops.
• Interest in building systems that actually get used by wet‑lab teams.

• Experience with diffusion, flow matching, or other modern generative methods.
• Experience designing or analyzing biological datasets.
• Exposure to model retraining from experimental or real‑world feedback.