Research Intern, Machine Learning

onepot • South San Francisco, CA

1d • Onsite

About The Position

onepot is automating chemistry by combining AI and advanced robotics in a self-improvement loop: AI systems design experiments, robotic systems execute them, and the resulting data improves future models. The company develops chemistry models for tasks including reaction planning and outcome, input material costs, and mass spectra prediction. Many models are state-of-the-art and use proprietary datasets, and are deployed internally in real workflows with close feedback between usage, data, and model training.

The Research Intern, Machine Learning will help train the next generation of chemistry models and enable synthesis of previously inaccessible molecules. The work spans the full modeling stack, including collaborating with lab staff on data acquisition, training models of different types and sizes, and deploying models into experimental workflows. The intern will also build and help maintain training infrastructure and abstractions, including data pipelines and system tasks such as parallelism strategies and quantization. The position does not involve lab work, though some projects may require understanding lab workflows. The role is fully in person at onepot’s South San Francisco lab.

Requirements

• Strong fundamentals in machine learning and a deep understanding of modern empirical and experimental machine learning.
• Experience developing novel model-training techniques or working with novel tasks or datasets.
• Familiarity with PyTorch or other machine-learning frameworks.
• Knowledge of basic machine-learning theory.
• Enthusiasm for working across the machine-learning stack, including data, training, inference, and deployment.
• Comfort working across disciplines and learning unfamiliar technical areas as needed.
• 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.

Nice To Haves

• Background in a deeply quantitative field; experience training machine-learning models is ideal.
• Experience in a startup, research group, competition team, or another environment involving significant ownership and limited resources.
• Experience training LLM models, particularly mid-training and post-training.
• Machine-learning systems experience, including scaling, kernels, or parallelism.
• Familiarity with chemistry models such as retrosynthesis or mass-spectrometry modeling, or with cheminformatics.
• Experience with active learning or other techniques suited to low-data regimes.

Responsibilities

• Help train the next generation of chemistry models.
• Work with lab staff on data acquisition.
• Train models of different types and sizes.
• Deploy models directly into experimental workflows.
• Build and help maintain infrastructure and abstractions for training diverse models, including data pipelines.
• Contribute to system tasks such as parallelism strategies and quantization.
• Support the development of models with advanced chemistry intuition and experimental capabilities.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: No Education Listed