Scientific Machine Learning Engineer
New York Mid-level Machine Learning Engineering
At a glance
Mid-level Machine Learning Engineering role at Schrödinger. New York · $130,000–$160,000 base.
SalaryStated by Schrödinger
$130,000 – $160,000
Base salary for this role, in USD per year, as published in the posting.
This role, from SchrödingerOur estimate for the market: $166,050–$230,000Median $218,200
Below the market median of $218,200 for mid-level machine learning engineering roles in New York.
Worked out from 25 postings that stated pay, median $218,200. Last updated Oct 20, 2026.
RoleMachine Learning Engineering
SeniorityMid-level
LocationNew York
Base pay$130,000–$160,000
PostedSep 30, 2026 · 3w ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Build and test software for virtual screening, linking machine-learning systems with physics-based tools such as docking
- Work with research scientists to adapt workflows for new discovery needs and extend methods already in use
- Read scientific papers and test newer ML approaches to improve hit discovery accuracy and applicability
What you bring
- A bachelor’s degree or higher in chemistry, physics, computer science or a related field
- One to three years of experience building production software
- You know ML methods for predicting measurable biological or chemical properties, and can program in Python
- You have used PyTorch or another machine-learning framework
Who this fits
This role suits an applied engineer who enjoys scientific problem-solving, large codebases and datasets, and close work with researchers. It is based in New York, with a stated base salary range of $130,000 to $160,000 per year.
From the employer
We’re seeking a Scientific Machine Learning (ML) Engineer to join us in our mission to transform the discovery of therapeutics and materials!
Our Hit Discovery team builds computational workflows that combine machine learning with chemical physics to efficiently evaluate billions of potential drug molecules. As a member of this group, you’ll collaborate with research scientists on improving the accuracy and applicability of these methods and turn them into robust, scalable software products. Who will love this job:- An applied problem solver who’s excited about addressing challenging problems in computational chemistry and thrives when working with large codebases and data sets
- A growth-oriented engineer who’s eager to adopt best practices like automated testing and code reviews
- A collaborative communicator who enjoys bridging the gap between ML, software engineering, and scientific research
What you’ll do:
- Design, build, and test scalable software for ML virtual screening pipelines, integrating ML applications with physics-based backends (such as docking and free energy perturbation) to optimize hit and hit-to-lead campaigns
- Work closely with scientists to adapt and expand our existing workflows for new discovery cases, as well as help develop new methodologies with our current pipelines
- Explore scientific literature and benchmark novel ML approaches to improve the applicability and accuracy of core hit discovery workflows
What you should have:
- Bachelor’s degree or higher in Chemistry, Physics, Computer Science, or a related field
- Between one and three years of production-level software development experience
- Prior ML knowledge (i.e., Reinforcement Learning or Active Learning) for predicting measurable properties of biological and/or chemical systems
- Python programming proficiency and experience with ML frameworks like PyTorch
We’d love to hire someone with:
- Familiarity with one of the following areas: molecular dynamics simulation, free energy calculation methods, ligand-based drug design, cheminformatics, quantum mechanics, virtual screening, and/or structured modeling
- Experience with development of relevant commercial and/or open-source computational chemistry software