Research Intern, Machine Learning

About the Role

onepot is automating chemistry by combining AI and advanced robotics to enable a self-improvement loop. We are building a small, unusually ambitious team and are looking for a machine learning researcher to join us to help train the next generation of chemistry models. Our models are trained for a wide range of tasks, including reaction planning and outcome, input material costs, and mass spectra prediction.

Key Responsibilities

• Help train the next generation of chemistry models.
• Enable synthesis of previously inaccessible molecules.
• Span the full modeling stack.
• Work with lab staff on data acquisition.
• Train models of many different shapes and sizes.
• Deploy models directly into experimental workflows.
• 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.
• Train models with superhuman chemistry intuition and experimental capabilities.

Requirements & Tech Stack

• 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.
• Curiosity and excitement about chemistry.
• A strong bias toward building, testing, and learning from real systems.
• Ability to work extended hours and weekends as necessary.
• Ability to work safely in an active chemistry laboratory and around scientific equipment.
• Note: This position does not involve lab work, but some projects may require an understanding of lab workflows.

Potentially Relevant Experience

Experience in any of the following areas would be useful, but we do not expect one person to have all of it:

• Training LLM models (particularly mid- and post-training).
• Machine learning systems (scaling, kernels, parallelism, etc.).
• Familiarity with chemistry models (retrosynthesis, mass spec modeling, etc.) or cheminformatics.
• Active learning or other techniques suited for low-data regimes.

Qualifications

• 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.
• onepot works fully in person in our South San Francisco lab.