Machine Learning Researcher - Agentic Science

PostEra; United States; Remote; USD 150.000 - 210.000; Vollzeit

Zusammenfassung

PostEra is building an AI-first biotech company focused on accelerating drug discovery. This role develops the agentic research vertical, creating quantitative models of biochemical processes and analyzing biological data to inform decisions.

You will advance in-context learning and foundation models from proprietary multimodal data, translating research into capabilities used by PostEra's scientists.

Qualifikationen

• PhD in machine learning or STEM research involving ML

Aufgaben

• Develop and benchmark agentic systems to automate quantitative models of biological processes.
• Lead research projects on in-context learning, agentic systems, and foundation models.
• Collaborate with scientists to align modeling with potency, ADME, and early clinical design.
• Publish results in ML, medicinal chemistry, or computational biology venues.

Kenntnisse

In-context learning

Few-shot learning

Tabular learning

Molecular machine learning

Agentic workflows for science

Python

Ausbildung

PhD in ML or related STEM with ML focus

Tools

PyTorch

JAX

Python

Jobbeschreibung

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. PostEra also advances small molecule programs through partnerships with pharma. We've closed over $1B in AI partnerships including 4 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. PostEra's Organizational Structure We believe time spent navigating complex hierarchies in organizations is better invested in the pursuit of novel technology and scientific discoveries. As such, our organizational structure is intentionally minimalist. We promote a culture where individual achievement is celebrated through proportional compensation and internal recognition, including meaningful promotions. Ultimately, our collective focus is delivering cures to patients, which we believe needs a huge amount of cross-disciplinary collaboration, so we've fitted our organizational structure to serve that end.

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 analyse biological data for target validation. You will interact closely with chemists and biologists to use these models to drive drug discovery decisions. You will also develop machine learning methods that can rapidly adapt to new drug discovery problems from limited labeled data. A particular focus is molecular and tabular in-context learning and building foundation models from PostEra's proprietary multimodal data. You will build models that use the context of drug discovery effectively, determine which prior examples and tasks are relevant, quantify when transfer is helpful or harmful, and provide reliable predictions under distribution shift. You will help drive the full research loop: defining tasks, constructing datasets and evaluation episodes, developing strong baselines, training and scaling models, performing 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 work closely with medicinal chemists, computational chemists, and other scientists.

Key Responsibilities

• Agentic Research for Chemistry and Biology: Develop and benchmark agentic systems to automate the development of quantitative models to simulate biological and physiological processes, and the analysis of biological data.
• Research Direction and Execution: Independently identify, formulate, and lead research projects involving in-context learning, agentic systems, few-shot adaptation, tabular foundation models, and molecular machine learning.
• In-Context Learning for Molecules: Design and train models that adapt to new assays, endpoints, targets, or chemical series using limited labeled context and heterogeneous historical data.
• Benchmarking: Rigorously compare new approaches against strong baselines, curating test cases that deconfounds impact of different sources of bias.
• Cross-Functional Collaboration: Work with scientists to connect modeling objectives and evaluation metrics to practical decisions in potency modeling, ADME prediction, selectivity, lead optimization, and the design of early clinical studies.
• Model Training and Scaling: Develop efficient training and data pipelines and, where appropriate, scale models across large collections of molecular and tabular tasks.
• Research Engineering: Produce readable, reproducible research code; maintain well-tracked experiments; and contribute through code review, documentation, and shared modeling infrastructure.
• Scientific Dissemination: Publish results in leading machine learning, medicinal chemistry, or computational biology venues. You will be an ambassador of PostEra to the scientific community.

Key Requirements

• PhD degree in machine learning, or STEM research involving the development of novel machine learning approaches
• Track record of high-quality research, such as publications and open-source contributions
• Strong research or engineering experience in modern machine learning, deep learning, or statistical modeling, backed by understanding of the theory behind machine learning algorithms
• Demonstrated expertise in at least one relevant area: agentic workflows for science, machine learning approaches to bioinformatics and clinical data modelling, in-context learning, tabular learning, few-shot learning
• Hands-on experience training, debugging, and evaluating ML models in Python using frameworks such as PyTorch or JAX
• Ability to independently translate ambiguous scientific or technical problems…