Senior Machine Learning Scientist I, Model-Driven Optimization

Experteer Overview In this role you will advance the lab-in-the-loop protein optimization platform by developing ML methods and data strategies for sequential decision-making. You will work at the interface of ML, protein design, engineering, and experimental science to shape models and optimization systems that accelerate therapeutic discovery. Expect to tackle design, testing, and learning loops that directly impact what gets built and studied in the lab. You will collaborate with cross-functional teams to turn ideas into reliable, scalable capabilities and drive measurable experimental impact. This opportunity offers a chance to lead technically while contributing hands-on, with a clear mission to transform Compensation / Benefits * Develop new ML methods and systems for lab-in-the-loop protein optimization, including multi-objective optimization and property modeling * Shape data-generation and data-use strategies to maximize information gain and design improvement * Build and apply LLM-enabled and agentic workflows to explore hypotheses and accelerate iterative learning * Design, implement, test, and maintain production-quality ML models, software components, and data workflows with reliability and efficiency * Collaborate with ML engineering and software teams to deliver robust platform capabilities with clear ownership * Work with protein designers and wet-lab scientists to ensure models align with experimental reality and deliver impact * Identify technical gaps, propose milestones, align stakeholders, and set direction across cross-functional programs * Communicate across disciplines to raise technical standards in ML, engineering, protein design, and experiments Tasks * PhD in relevant quantitative field * Strong practical experience with probabilistic ML, Bayesian optimization, active learning, or experimental design for sequential decision-making under uncertainty * Experience building ML methods or systems for biological/experimental data, handling noisy assays and data-generation strategies * Ability to translate ML ideas into usable systems, tools, or workflows that influence decisions * Strong Python skills and experience with PyTorch, JAX, or similar frameworks * Systems thinking and ability to design interfaces and collaborate with engineering teams * Excellent communication skills bridging ML, engineering, protein design, and experiments * Pragmatic, collaborative, structured approach to open-ended problems in fast-moving environments Key requirements * base salary $192,000-$265,000 USD * annual bonus * equity compensation * competitive benefits package

_ LLM-enabled and agentic workflows to explore hypotheses and accelerate iterative learning * Design, implement, test, and maintain production-quality ML models, software components, and data workflows with reliability and efficiency * Collaborate with ML engineering and software teams to deliver robust platform capabilities with clear ownership * Work with protein designers and wet-lab scientists to ensure models align with experimental reality and deliver impact * Identify technical gaps, propose milestones, align stakeholders, and set direction across cross-functional programs * Communicate across disciplines to raise technical standards in ML, engineering, protein design, and experiments Tasks * PhD in relevant quantitative field * Strong practical experience with probabilistic ML, Bayesian optimization, active learning, or experimental design for sequential decision-making under uncertainty * Experience building ML methods or systems for biological/experimental data, handling noisy aaaa to and data-generation strategies * Ability to translate ML ideas into usable systems, tools, or workflows that influence decisions * Strong Python skills and experience with PyTorch, JAX, or similar frameworks * Systems thinking and ability to design interfaces and collaborate with engineering teams * Excellent communication skills bridging ML, engineering, protein design, and experiments * Pragmatic, collaborative, structured approach to open-ended problems in fast-moving environments Key requirements * base salary $192,000-$265,000 USD * annual bonus * equity compensation * competitive benefits package