About Oumi
Why we exist
Oumi is on a mission to make frontier AI truly open for all. We are founded on the belief that AI will have a transformative impact on humanity, and that developing it collectively, in the open, is the best path forward to ensure that it is done efficiently and safely.
What we do
Oumi provides an all-in-one platform to build state-of-the-art AI models, end to end, from data preparation to production deployment, empowering innovators to build cutting-edge models at any scale. Oumi also develops open foundation models in collaboration with academic collaborators and the open community.
Our Approach
Oumi is fundamentally an open-source first company, with open-collaboration across the community as a core principle. Our work is:
- Open Source First: All our platform and core technology is open source
- Research-driven: We conduct and publish original research in AI, collaborating with our community of academic research labs and collaborators
- Community-powered: We believe in the power of open-collaboration and welcome contributions from researchers and developers worldwide
Role Overview
Research Interns are an integral part of Oumi's research team, focusing on advancing the state-of-the-art in large language models (LLMs), vision language models (VLMs), and related technologies. The role involves conducting cutting-edge research, contributing to open-source projects, and collaborating with other researchers and engineers. Research Interns at Oumi will work on various aspects of LLM/VLM development including training, evaluation, data curation, and benchmark development.
We welcome interns anytime throughout the year!
What you’ll do
- Model Development: Conduct research on training and evaluating new Large language models (LLMs), Vision Language Models (VLMs), and other AI models. This includes exploring new architectures, training techniques, and optimization methods.
- Data Curation: Develop methodologies for curating high-quality datasets for training and evaluating LLMs. This may involve data synthesis and other novel techniques.
- Benchmark Development: Develop evaluation benchmarks to measure the performance of LLMs across various tasks and domains.
- Research and Experimentation: Design and conduct experiments to validate research hypotheses and improve model performance.
- Open Source Contribution: Contribute to the Oumi open-source platform, models and projects, and other relevant tools and libraries.
- Collaboration: Collaborate with other researchers, engineers, and the broader community to advance the field of open-source AI.
- Publication: Publish research findings in leading conferences and journals.
- Platform Evaluation: Evaluate existing models and identify areas of improvement.
- Flexibility: Work with various models, including text and multimodal models, and both open and closed models.
- Problem Solving: Focus on the research that matters by skipping the plumbing and moving straight to research, building on the work of others and contributing back.
What you’ll bring
- Education: Currently enrolled in a Ph.D. or MSc. in computer science, machine learning, artificial intelligence, or a related field is preferred. Candidates with a strong publication record, or equivalent industry experience will be preferred.
- Research Experience: Experience in conducting original research in machine learning, with a publication record in top-tier conferences or journals.
- ML Expertise: Deep understanding of machine learning and deep learning concepts, with specific knowledge of large language models (LLMs) and/or vision language models (VLMs).
- Programming Skills: Strong programming skills in Python and experience using deep learning frameworks (e.g. PyTorch).
- Open Source: Familiarity with open-source projects and a passion for contributing to the open-source community.
- Initiative: A self-starter who can work independently and take ownership of initiatives.
- Values: Share Oumi's values: Beneficial for all, Customer-obsessed, Radical Ownership, Exceptional Teammates, Science-grounded.
The Benefits
- Competitive pay
- Regular team offsites and events
