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PostEra
PostEra
11 – 50 Employees
Artificial IntelligenceBiotechnologyHealthcare
PostEra is a biotechnology and artificial intelligence company focused on improving how new medicines are discovered. Its Proton platform applies machine learning to medicinal chemistry, helping researchers address complex challenges in drug discovery and accelerate the development of potential therapies. Founded in 2020, PostEra works with pharmaceutical organizations including Pfizer and Amgen on AI-enabled approaches to drug development.
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Machine Learning Researcher, Agentic Science at PostEra (Remote)

PostEra is seeking a Machine Learning Researcher to build agentic systems and adaptive models for AI-driven biotechnology. The role applies computational methods to medicinal chemistry and drug discovery.

USUnited States
$200,000 – $300,000 / year
RemoteFull-timeMiddleSeniorEnglish RequiredAI Research ScientistMachine Learning Engineer
PythonPyTorch

Description

  • Build PostEra’s agentic research function by automating mechanistic modeling of biochemical and physiological processes
  • Analyze biological data for target validation and inform drug discovery decisions with machine learning models
  • Create machine learning methods that adapt to new drug discovery challenges with limited labeled data
  • Develop molecular and tabular in-context learning systems and foundation models using proprietary multimodal data
  • Assess relevant prior examples and tasks, measure when transfer helps or harms, and deliver reliable predictions under distribution shift
  • Define tasks, build datasets and evaluation episodes, establish baselines, train and scale models, run rigorous ablations, and translate effective methods into scientific capabilities
  • Develop and benchmark agentic systems for quantitative biological and physiological modeling
  • Lead independent research projects in in-context learning, agentic systems, few-shot adaptation, tabular foundation models, and molecular machine learning
  • Design and train models that adapt to new assays, endpoints, targets, and chemical series
  • Evaluate methods against strong baselines and create test cases that account for bias sensitivity
  • Work with scientists on potency modeling, ADME prediction, selectivity, lead optimization, and early clinical study design
  • Build efficient training and data pipelines while scaling models across molecular and tabular problems
  • Write readable, reproducible research code; document experiments; and support code review and shared modeling infrastructure
  • Publish findings in leading machine learning, medicinal chemistry, or computational biology venues and represent PostEra within the scientific community

Requirements

  • PhD in machine learning or in a STEM research field focused on developing novel machine learning methods
  • Evidence of high-quality research through publications, open-source contributions, or comparable work
  • Strong research or engineering background in modern machine learning, deep learning, or statistical modeling, with a solid grasp of algorithmic theory
  • Expertise in at least one relevant area, including agentic scientific workflows, machine learning for bioinformatics or clinical data, in-context learning, tabular learning, or few-shot learning
  • Practical experience training, debugging, and evaluating machine learning models in Python with frameworks such as PyTorch or JAX
  • Ability to turn ambiguous scientific or technical questions into well-defined machine learning projects with datasets, tasks, baselines, metrics, and validation plans
  • Ability to design rigorous experiments, benchmarks, and ablations that separate genuine gains from bias and identify the sources of model improvements
  • Comfort working in a startup where priorities change, data may be imperfect, and sound judgment is as important as model complexity
  • Drug discovery experience is not required
  • Preferred: experience training tabular foundation models for sparse, heterogeneous, small-data, or high-missingness settings
  • Preferred: experience developing molecular in-context learning systems or adapting general-purpose in-context models to molecular or scientific data
  • Preferred: experience training large models, including 1B-plus parameter systems, distributed training, sharding, data and model parallelism, and large-scale data pipelines
  • Preferred: experience developing AI co-scientist systems for physical or biological problems
  • Preferred: hands-on machine learning experience with biological, biochemical, or clinical data
  • Preferred: experience moving research models into production scientific software or computational workflows

Benefits

  • Equity allocation of 0.05% to 0.1%
  • Compensation adjusted proportionally
  • Recognition within the organization
  • Opportunities for meaningful promotion

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