Machine Learning Researcher - Agentic Science

Hybrid (US)$200k - $300kFull-timePosted 2w ago

About the role

Role focused on developing agentic systems and foundation models to automate mechanistic modeling and data analysis for drug discovery at PostEra. Collaborates with chemists/biologists to drive decisions, with emphasis on in-context learning, few-shot adaptation, and scalable ML pipelines in molecular and tabular domains.

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 early clinical study design.
Model Training and Scaling: Develop efficient training and data pipelines and scale models across large collections of molecular and tabular tasks.
Research Engineering: Produce readable, reproducible research code; maintain well-tracked experiments; contribute through code review, documentation, and shared modeling infrastructure.
Scientific Dissemination: Publish results in leading ML, medicinal chemistry, or computational biology venues; act as PostEra ambassador to the scientific community.

Qualifications

PhD degree in machine learning, or STEM research involving the development of novel machine learning approaches
Track record of high-quality research (publications and open-source contributions)
Strong experience in modern ML/deep learning or statistical modeling with solid theoretical understanding
Expertise in at least one area: agentic workflows for science, ML for bioinformatics/clinical data, in-context learning, tabular learning, few-shot learning
Hands-on Python ML experience with PyTorch or JAX
Ability to translate ambiguous problems into well-defined ML projects with datasets, tasks, baselines, metrics, and validation
Capability to design careful experiments, benchmarks, and ablations to isolate improvements from biases
Comfort in a startup environment with evolving priorities and imperfect data

Skills

Required:Agentic workflows for scienceIn-context learningPython

Nice to have:Tabular foundation modelsMolecular machine learning

Last updated Oct 7, 2026. Interested? Apply on the company site.

About PostEra

Medicinal Chemistry powered by Machine Learning

PostEra is building a modern 21st century biopharma. We're using our advances in machine learning to accelerate Medicinal Chemistry and bring more cures to patients. PostEra advances small molecule programs through partnerships with biopharma while also advancing its own internal pipeline. We've raised $26M from top investors and closed $1Bn in AI partnerships, signing multi-year agreements with Amgen, Pfizer and the NIH. PostEra also launched and led the world's largest open-science drug discovery effort; COVID Moonshot.

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