Job Description
📋 Description Develop ML methods and systems for lab-in-the-loop protein optimization. Shape data-generation strategies for informative campaigns and model improvement. Build LLM-enabled workflows to explore design hypotheses and connect models to data. Design, test, and maintain production-grade ML models, components, and data workflows. Partner with ML engineering and software teams to integrate components into platform capabilities. Collaborate with protein designers and wet-lab scientists to ground models in experiments. 🎯 Requirements PhD in ML, computational biology, CS, applied mathematics, engineering, or related quantitative field. Strong practical experience with probabilistic ML, Bayesian optimization, active learning, or sequential decision-making. Experience developing ML methods or systems for biological/experimental data with noisy assays and sparse labels. Demonstrated ability to translate ML ideas into systems, tools, or workflows that affect decisions. Strong Python skills and experience with PyTorch, JAX, or similar tools. Strong systems thinking; design interfaces; partner with engineering teams to build scalable ML infrastructure. 🎁 Benefits Annual bonus and equity compensation. Competitive benefits package. Opportunity to shape ML for protein design with real-world impact.
