ApplySarthiMachine Learning Force Field Scientist
Schrödinger
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Schrödinger has 39 open roles listed here.
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- Computational Materials Science, Formulations
- Computational Materials Scientist, Atomic Layer Processing and Chemical Reactivity
- Computational Chemist
- Computational Chemist, Target Druggability and Structure-Based Design
Counted across 14 company job boards, updated as roles open and close.
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Interviews for scientist roles keep coming back to Python, Machine learning, SQL, LLMs. Practise those questions before you sit with Schrödinger.
Questions you are likely to be asked
- Why do you want to join Schrödinger?
- What is your experience with Machine learning? Tell me one thing you learned the hard way.
- How did you know your model was actually good, and not just good on your test set?
- Tell me about a time the data was messy or wrong. What did you do?
- How would you explain your model's result to someone who is not technical?
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Practise the Machine Learning Force Field Scientist at Schrödinger interview free →We’re seeking a Machine Learning (ML) Scientist to join us in our mission to transform the discovery of therapeutics and materials.
Schrödinger has pioneered a physics-based software platform that enables discovery of high-quality, novel molecules for drug development and materials applications more rapidly and at lower cost compared to traditional methods. The software platform is used by biopharmaceutical and industrial companies, academic institutions, and government laboratories around the world. Our multidisciplinary drug discovery team also leverages the software platform to advance collaborative programs and its own pipeline of novel therapeutics to address unmet medical needs.
As a member of our Machine Learning team, you’ll develop state-of-the-art ML force fields targeting impactful applications in Life and Materials sciences.
Who will love this job:
- An ML force fields expert who has developed, validated, and applied ML force fields to simulate complex condensed-phase systems, such as solid-liquid interfaces, reactive events in the condensed phase, or solvated biomolecules
- An innovator who’s driven to leverage technical knowledge to make a tangible impact
- A scientist with deep knowledge of both finite system and periodic DFT, as well as other electronic structure methods, and who understands the limitations and appropriate applications of these methods
- A proficient Python programmer with prior knowledge of ML toolkits such as PyTorch, Scikit-Learn, NumPy, SciPy, and Pandas
- An independent researcher who enjoys collaborating with an interdisciplinary team in a fast-paced environment
What you’ll do:
- Build and manage large data sets generated using quantum chemical methods at scale to develop predictive ML force fields
- Develop software that trains and applies ML force fields to challenging problems in life and materials sciences
- Extend the accuracy, capability and generalization of current ML force fields
- Communicate results and present ideas to the team
What you should have:
- A PhD (or extensive experience) in Chemistry, Materials Science, Engineering, Computer Science, or Physics
- A proven track record of scientific contribution and independent research
- Prior experience with development of ML force fields and/or electronic structure methods
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Listed on greenhouse · posted 2026-06-17. ApplySarthi collects openings and links to application pages; the role is advertised by Schrödinger, not by us.