Research Intern, Machine Learning (AI)

Location: South San Francisco, CA, United States; fully in person in the chemistry lab

Company

hirify.global combines AI and advanced robotics to automate chemistry through a self-improving loop in which AI designs experiments, robots execute them, and resulting data improves future models.

What you will do

• Train machine learning models for reaction planning and outcome prediction, input material costs, mass spectra prediction, and other chemistry tasks.
• Work with lab staff on data acquisition and deploy models directly into experimental workflows.
• Build and maintain infrastructure and abstractions for training diverse models, including data pipelines.
• Develop system capabilities such as parallelism strategies and quantization.
• Help enable the synthesis of previously inaccessible molecules through models with advanced chemistry intuition.

Requirements

• Strong machine learning fundamentals 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 and knowledge of basic machine learning theory.
• Enthusiasm for working across the full machine learning stack, including data, training, inference, and deployment.
• Comfort working across disciplines and learning unfamiliar technical areas.
• Strong written and verbal communication skills, curiosity about chemistry, and a bias toward building, testing, and learning from real systems.
• No formal education is required; a background in a deeply quantitative field and experience training machine learning models are ideal.
• Ability to work extended hours and weekends as necessary and work safely in an active chemistry laboratory around scientific equipment. The position does not involve lab work, but some projects may require understanding lab workflows.

Nice to have

• Experience in a startup, research group, competition team, or another environment involving significant ownership and limited resources.
• Training LLMs, particularly during mid-training and post-training.
• Machine learning systems experience, including scaling, kernels, or parallelism.
• Experience with chemistry models such as retrosynthesis or mass spectra modeling, or with cheminformatics.
• Experience with active learning or techniques suited for low-data regimes.

Culture & Benefits

• Work in a small, cross-disciplinary team of machine learning engineers, chemists, computer scientists, and hardware engineers.
• Models are trained on proprietary datasets and deployed internally in real chemistry workflows.
• Work is fully in person at the South San Francisco lab.
• The role may require extended hours and weekend work when necessary.