About the job
JOIN THE REVOLUTION AT MITHRL
Imagine a world where groundbreaking medicines reach those in need within months, not years, and where scientific innovations occur at lightning speed.
Mithrl is pioneering the world's first commercially available AI Co-Scientist, a transformative discovery engine that swiftly converts complex biological data into actionable insights. By posing questions in natural language, scientists receive rapid, meaningful analyses, innovative targets, hypotheses, and ready-to-file reports.
OUR IMPACT IS EVIDENT:
Achieved 12X year-over-year revenue growth
Trusted by top biotech firms and major pharmaceutical companies across three continents
Catalyzing substantial advancements from target identification to patient outcomes.
ROLE OVERVIEW
We are seeking a talented Machine Learning Engineer to focus on Analysis and Simulation to construct the foundational analytical and reasoning framework for the Mithrl AI Co-Scientist. Your contributions will shape how our AI interprets biological datasets, formulates scientific conclusions, and integrates simulation tools for drug discovery.
You will create reusable analysis modules that will be applied across all datasets and design complex workflows that merge statistical analysis, biological reasoning, and computational modeling. Additionally, you will explore and optimize simulation tools for small molecule discovery, including ADMET predictions, docking scores, Boltzmann generators, and various computational chemistry engines.
This position is critical in enhancing the intelligence of the AI Co-Scientist. If you possess a solid foundation in machine learning, computational biology, and scientific analysis workflows, and are eager to influence how AI interprets biological systems, we invite you to apply.
RESPONSIBILITIES
Develop AI-powered analysis agents capable of performing complex biological reasoning across diverse datasets.
Create a standardized analysis suite for each dataset, encompassing modules for differential expression, pathway analysis, feature importance, clustering, scoring, enrichment, and mechanism-of-action interpretation.
Engineer multi-step workflows integrating machine learning models, statistical logic, and biological expertise to yield high-confidence insights.
Design and implement intelligent reasoning strategies that enable Mithrl to execute numerous analyses per dataset.

