Machine Learning Researcher - Agentic Science; Fully Remote

• Develop the agentic research vertical at Post Era, using agentic systems to automate mechanistic models for biochemical and physiological processes.
• Build machine learning methods that rapidly adapt to new drug discovery problems from limited labeled data.
• Drive the full research loop: defining tasks, constructing datasets and evaluation episodes, training and scaling models, and translating methods into capabilities for scientists.

Key Responsibilities:

• Develop and benchmark agentic systems to automate quantitative model development and biological data analysis.
• Independently lead research projects in in-context learning, agentic systems, few-shot adaptation, and molecular machine learning.
• Collaborate cross-functionally with medicinal chemists and biologists to connect modeling objectives to practical drug discovery decisions.

Key Requirements:

• PhD in machine learning or a STEM field involving novel ML approaches, with a track record of high-quality research.
• Strong research and engineering experience in modern ML, deep learning, or statistical modeling, with hands-on skills in Python and frameworks like PyTorch or JAX.
• Ability to independently translate ambiguous scientific problems into well-defined ML projects and design careful experiments and benchmarks.

Post Era is building an AI-first biotech, using its Proton AI platform to accelerate the discovery of new medicines for patients. The company has a minimalist organizational structure, celebrating individual achievement through proportional compensation and collaboration toward delivering cures.