Senior Platform Engineer for AI-Driven Drug Discovery

Speedrun Talent Network; San Mateo, Northern (CA, KY); Hybride; USD 180 000 - 240 000; Plein temps

Avantages offerts par ce poste

Equity

Medical, dental, and vision insurance

401(k) plan

Résumé du poste

Genesis Molecular AI is building a world-class software team to solve problems in drug discovery through machine learning, biophysical simulation, and computational chemistry.

You will own the full-stack platform used daily by computational chemists, from data exploration to real-time visualization, shipping AI-native workflows and modern interaction patterns that shorten the loop between hypothesis and insight.

Qualifications

• Strong analytical thinking and ability to reason from first principles.
• Experience owning problems across frontend, backend, data pipelines, and infrastructure.
• Proven track record shipping production code and delivering end-to-end projects.
• Motivation to work closely with users and design simple, scalable solutions.
• Interest at the intersection of software, drug discovery, and machine learning.

Responsabilités

• Own and evolve the full-stack platform used by computational chemists, from data exploration to real-time visualization.
• Build tools for exploring and visualizing high-dimensional datasets of molecules, proteins, and model predictions.
• Design flexible systems for running complex chemical workflows with various computational tools and ML models.
• Collaborate with ML engineers and chemists to productionize new computational methods.
• Scale data infrastructure to handle billions of datapoints and thousands of parallel jobs.

Description du poste

Genesis Molecular AI is building a world-class software team to solve problems in drug discovery through machine learning, biophysical simulation, and computational chemistry.

You will own the full-stack platform used daily by computational chemists, from data exploration to real-time visualization, shipping AI-native workflows and modern interaction patterns that shorten the loop between hypothesis and insight.