Senior Machine Learning Researcher

Deep Origin is a biotechnology company accelerating drug discovery through AI-powered tools. Our platforms simplify R&D, simulate biology, and empower scientists to solve diseases and extend healthspan.

We are looking for a Senior ML Engineer with experience in creating predictive models, particularly in the biology space. You will help to create ML-based representations of specific biological systems, as well as take part in tuning and optimization of larger composite models, with the goal of creating a multiscale biological simulator for drug response prediction. You will also work with the Cellular Simulations team to incorporate biological simulations and mechanistic insights into the ML-based approaches.

Requirements

• B.S. or M.S. in a relevant quantitative field (Computer Science, Math, Physics, etc.).

• At least 5 years of experience in the development of machine learning models in an industry setting.
• Knowledge of optimization methodologies for machine learning models.
• Extensive coding experience, preferably Python, but other language proficiency will be considered depending on experience.
• Fluent English for collaboration with an international team.

Nice to have:

• Ph.D. in relevant field.
• Deep experience in biological modeling and simulation, in particular in a systems biology or molecular level context.
• Experience with ML tasks involving small datasets.
• Experience in optimization of “composite models” - connected models that share output/input.

Responsibilities

• Construct ML-based representations of biological systems, in particular signaling pathway dynamics in cells
• Help to create a hybrid multiscale biological simulator, incorporating ML components for more effective model calibration and simulation

• Plan and organize work to ensure specific deadlines and milestones are met, coordinating with others to ensure work is correctly aligned and integrated with other efforts. 
• Communicate effectively within the company and external teams, updating others frequently on progress and bottlenecks.