Machine Learning Researcher - Agentic Science
PostEra (Postera) · Remote
About this role
## Machine Learning Researcher — Agentic Science **About PostEra** PostEra is building an AI-first biotech. We use **Proton**, our AI platform for medicinal chemistry, to accelerate the discovery of new medicines for patients. PostEra is advancing an internal pipeline focused on **Women’s Health and Fertility**, and has used Proton to nominate multiple clinical candidates across PMOS and Fertility. We also advance small molecule programs through partnerships with pharma, and have closed **$1B+** in AI partnerships (including multi-year agreements with **Pfizer** and **Amgen**). PostEra is also leading an **antiviral drug discovery center** for pandemic preparedness, funded by one of the largest grants in NIH history. **Role Overview** In this role, you will develop the **agentic research vertical** at PostEra—using agentic systems to automate the development of **mechanistic models** for biochemical and physiological processes, and to analyze biological data for **target validation**. You’ll work closely with chemists and biologists to turn these models into drug discovery decisions. You will also build machine learning methods that can **rapidly adapt** to new drug discovery problems from **limited labeled data**, with a particular focus on: - **Molecular and tabular in-context learning** - Building **foundation models** from PostEra’s proprietary **multimodal** data - Determining which prior examples/tasks are relevant - Quantifying when transfer is helpful vs. harmful - Providing reliable predictions under **distribution shift** You’ll help drive the full research loop: defining tasks, constructing datasets and evaluation episodes, building strong baselines, training and scaling models, running rigorous ablations, and translating successful methods into capabilities used by PostEra’s scientists. **Prior drug discovery experience is not required**—but you should be motivated to learn the domain and collaborate closely with medicinal/computational chemists and other scientists. --- ## Key Responsibilities ### Agentic Research for Chemistry and Biology - Develop and benchmark agentic systems to automate quantitative modeling of biological/physiological processes - Analyze biological data to support discovery workflows ### Research Direction and Execution - Independently identify, formulate, and lead research projects involving: - in-context learning - agentic systems - few-shot adaptation - tabular foundation models - molecular machine learning ### In-Context Learning for Molecules - Design and train models that adapt to new assays/endpoints/targets/chemical series using limited labeled context and heterogeneous historical data ### Benchmarking - Rigorously compare new approaches against strong baselines - Curate test cases that deconfound bias from different sources ### Cross-Functional Collaboration - Partner with scientists to connect modeling objectives and evaluation metrics to practical decisions in: - potency modeling - ADME prediction - selectivity - lead optimization - early clinical study design ### Model Training and Scaling - Build efficient training and data pipelines - Scale models across large collections of molecular and tabular tasks (where appropriate) ### Research Engineering - Produce readable, reproducible research code - Maintain well-tracked experiments - Contribute via code review, documentation, and shared modeling infrastructure ### Scientific Dissemination - Publish results in
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