Quantitative Analytics Program, Applied Computational Intelligence

What you'll do

• Develop and deploy AI-powered advisors and decision support systems using customer and market data.
• Build Generative AI assistants and multi-agent systems for workflow orchestration and task execution.
• Create enterprise knowledge platforms using Retrieval-Augmented Generation (RAG) and Large Language Models.
• Train, fine-tune, and evaluate transformer-based models, foundation models, and small language models.
• Build and optimize scalable model training and deployment pipelines using distributed computing.
• Monitor production models to evaluate performance, stability, and model drift through analytics frameworks.
• Apply statistical and quantitative techniques to validate model design, calibration, and implementation.
• Optimize inference speed, latency, and cost for production AI systems using distillation and quantization.

What we're looking for

• Must have 6+ months of work experience, training, military experience, or education.
• Currently pursuing a Masters degree in Computer Science, Machine Learning, AI, Engineering, or a related quantitative field (preferred).
• Expected graduation date must be between December 2026 and June 2027 (preferred).
• Must have strong programming experience with tools such as Python, R, SQL, Java, Spark, or similar technologies.
• Must have hands-on experience developing machine learning and AI solutions in research, academic, or industry environments.
• Must have experience with supervised fine-tuning, post-training methodologies (RLHF, PPO, DPO, GRPO), and cloud deployment (GCP).
• Must have experience with multi-agent architectures, orchestration frameworks (LangChain, LangGraph, CrewAI), and RAG applications.
• Must have experience with distributed GPU training and efficient model tuning approaches like LoRA and PEFT.