Eloquent AI Fellowship Program
San Francisco • FullTime
Posted 1y ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
Eloquent AI is seeking talented individuals for its AI Fellowship Program, a 12-week immersive experience designed for STEM PhDs, post-docs, and early-career AI researchers. This program offers hands-on training in developing, deploying, and optimizing AI agents for complex workflows in regulated, high-stakes environments, particularly within financial services. Fellows will gain exposure to AI model development, MLOps, and conversational AI systems, working alongside AI engineers, product managers, and researchers. The fellowship aims to equip participants with the skills needed for leadership roles in AI product development, engineering, or research, with opportunities to collaborate on cutting-edge research projects.
Responsibilities
- Develop and deploy AI-powered agents using LLMs, RAG, and enterprise automation workflows.
- Gain hands-on experience in AI infrastructure, including LLMOps, MLOps, cloud deployment, and model optimization.
- Work on full-stack AI applications, collaborating with engineers and PMs to build scalable AI-driven products.
- Translate AI research into practical applications, integrating advances in language models, embeddings, and retrieval techniques.
- Collaborate directly with Eloquent AI's leadership and top AI practitioners.
Requirements
- Current or completed PhD or MSc degree in Computer Science, Engineering, Mathematics, Physics, or a related STEM field.
- Strong mathematical foundation, particularly in statistics, linear algebra, and optimization techniques.
- Programming experience, ideally in Python, with familiarity in ML frameworks like PyTorch and TensorFlow.
- Interest in AI product development, data science, or machine learning engineering.
- Ability to work in a fast-paced, collaborative AI-driven environment.
- Experience with LLMs, NLP, or Retrieval-Augmented Generation (RAG).
- Contributions to open-source AI projects or published research in AI/ML conferences.
- Hands-on experience with LLMOps, MLOps, cloud-based AI infrastructure, or AI deployment at scale.
- Experience in AI strategy, product management, or business applications of AI.