Applied ML Engineer for Drug Discovery & Simulations

Schrödinger; New York (NY); On-site; USD 130,000 - 160,000; Full time

Benefits offered by this job

Competitive salary

Equity-based compensation

Healthcare & dental/vision

401k

Flexible work schedule

Parental leave

Paid vacation

Job summary

Schrödinger seeks a Scientific Machine Learning (ML) Engineer to advance computational chemistry pipelines in New York. You will design scalable ML software that interfaces with physics-based backends to optimize hit-to-lead campaigns, and work with scientists to expand workflows for new discovery scenarios.

Ideal candidates have 1–3 years of production-level software experience, Python proficiency, and ML knowledge (RL/Active Learning).

Qualifications

• Bachelor’s degree or higher in chemistry, physics, CS, or related field.
• 1–3 years of production-level software development experience.
• Prior ML knowledge (RL or active learning) for predicting properties.
• Python proficiency and experience with ML frameworks like PyTorch.

Responsibilities

• Design, build, and test scalable ML software for screening pipelines.
• Collaborate with scientists to adapt workflows for new discovery cases.
• Benchmark ML approaches to improve hit discovery accuracy.

Skills

ML knowledge (RL/Active Learning)

Python programming

Python libraries

Education

Bachelor’s degree or higher in chemistry, physics, CS, or related field

Tools

PyTorch

Python

Job description

Schrödinger seeks a Scientific Machine Learning (ML) Engineer to advance computational chemistry pipelines in New York. You will design scalable ML software that interfaces with physics-based backends to optimize hit-to-lead campaigns, and work with scientists to expand workflows for new discovery scenarios.

Ideal candidates have 1–3 years of production-level software experience, Python proficiency, and ML knowledge (RL/Active Learning).