In this position, you will help train the next generation of chemistry models, and enable synthesis of previously inaccessible molecules.

Your work will span the full modeling stack. You will work with lab staff on data acquisition, train models of many different shapes and sizes, and deploy models directly into experimental workflows. You will build and help maintain infrastructure and abstractions for training a diverse set of models; this includes data pipelines and various system tasks such as parallelism strategies and quantization.

Ultimately, your role will be to train models with superhuman chemistry intuition and experimental capabilities.

No formal education is required. Ideal candidates will have some background in a deeply quantitative field, ideally with experience training ML models.

Strong fundamentals in machine learning with a deep understanding of modern empirical/experimental ML

Experience developing novel model training techniques or dealing with novel tasks or datasets

Experience in a startup, research group, competition team, or other environment where you had significant ownership and limited resources is particularly relevant

Familiarity with PyTorch or other machine learning frameworks

Knowledge of basic machine learning theory

Enthusiasm about working across the entire machine learning stack (data, training, inference, deployment)

Comfort working across disciplines and learning unfamiliar technical areas as necessary

Strong written and verbal communication skills

Research Intern, Machine Learning

Responsibilities

Qualification

Required

Preferred

Company

Onepot

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