Responsibilities
- Building infrastructure to facilitate the next generation of LLM and agentic research.
- Creating AI tools to facilitate scientific discovery in domains such as biology, cancer research, neuroscience, social science, etc.
- Designing, building, and training machine learning or language models for agentic workflows.
- Bridging the gap between cutting-edge research and a widely adopted product.
- Bringing software engineering best practices to a research environment.
- Supporting and collaborating with an open-source community.
- Releasing contributions as open source software, model releases, additions to Ai2’s public API and open research datasets, and technical reports.
Requirements
- Bachelor’s degree in CS/EE/Data Science/Applied Mathematics/Statistics/ML/NLP, or related field, or equivalent experience.
- 2+ years building agentic infrastructure that handles tools, skills, and other artifacts.
- 2+ years building infrastructure for data preprocessing/transformation and machine learning model training, evaluation, inference, and deployment.
- Knowledge of modern deep learning, natural language processing, and reinforcement learning techniques.
- Strong software engineering skills, particularly building performant systems and debugging.
- Experience with Python and PyTorch/Jax/TensorFlow, agentic frameworks (e.g., MCP).
- Familiarity with cloud compute resources (GCP, AWS, Modal) and containerization (Docker).
Nice-to-Haves
- Advanced degree in CS/EE/Data Science/Applied Mathematics/Statistics/ML/NLP or related fields.
- Contributions to open-source ML or research libraries (e.g., spaCy, AllenNLP, transformers, langchain).
- Experience operating at scale in production settings.
- Experience in HPC settings.
Compensation
Base salary range: $118,800 - $178,200, plus generous bonus plans.