Applied AI Fellowship — 12-Week Rotations

eloquentai; San Francisco (CA); On-site; USD 42,000 - 54,000; Full time

Job summary

Eloquent AI in San Francisco is offering a 12-week Fellowship for STEM graduates and PhDs to accelerate careers in applied AI. The program provides hands-on exposure to AI engineering, applied ML, and AI-driven product management through structured rotations.

You will work with AI engineers, product managers, and researchers to train, deploy, and optimize AI agents, while gaining exposure to AI evaluation, MLOps, and scalable testing frameworks.

Qualifications

• PhD or MSc in Computer Science, Engineering, Mathematics, Physics, or related field.
• Strong mathematical foundation including statistics and linear algebra.
• Proficiency in Python with ML frameworks (PyTorch, TensorFlow).
• Interest in AI product development, data science, or ML engineering.
• Ability to thrive in a fast-paced, collaborative AI-driven environment.

Responsibilities

• Develop and deploy AI-powered agents using LLMs, RAG, and enterprise automation workflows.
• Gain hands-on experience with AI infrastructure including LLMOps, MLOps, cloud deployment, and model optimization.
• Contribute to full-stack AI applications with engineers and PMs.
• Translate AI research into practical enterprise solutions leveraging language models and retrieval techniques.
• Collaborate with leadership and fellow AI practitioners to drive impact.

Skills

Python

Statistics

Linear Algebra

Optimization

ML/DL concepts

Education

PhD or MSc in STEM

Tools

PyTorch

TensorFlow

LLMOps

Cloud platforms (AWS/GCP)

Job description

Eloquent AI in San Francisco is offering a 12-week Fellowship for STEM graduates and PhDs to accelerate careers in applied AI. The program provides hands-on exposure to AI engineering, applied ML, and AI-driven product management through structured rotations.

You will work with AI engineers, product managers, and researchers to train, deploy, and optimize AI agents, while gaining exposure to AI evaluation, MLOps, and scalable testing frameworks.