Research Fellow position that explores the economic implications of AI technologies. Designed for PhD students and early - career researchers in related social sciences.
About the role
- Applied Scientist leveraging applied research to design production AI systems for Fortune 500 companies. Focusing on knowledge-centric AI, graph-based learning, and advanced RAG architectures.
Responsibilities
- Conduct applied research to solve real-world problems using LLMs, graph-based models, and multimodal AI.
- Rapidly understand problem context, constraints, and success metrics, and design pragmatic AI solutions aligned with product and business goals.
- Design hybrid AI architectures combining knowledge graphs, vector search, and deep learning for reasoning-aware systems.
- Research and implement graph embeddings, graph attention networks (GATs), and graph neural networks (GNNs) for representation learning and inference.
- Design and build advanced RAG systems at scale, going beyond naïve vector similarity search.
- Implement hybrid semantic retrieval across vector stores and graph databases (e.g., entity-aware retrieval, path-based reasoning, graph-augmented RAG).
- Optimize retrieval pipelines for latency, relevance, grounding, and explainability in production environments.
- Fine-tune LLMs and embedding models for domain-specific tasks (instruction tuning, adapters, LoRA, etc.).
- Design and implement LLM agent systems, including multi-agent orchestration strategies, tool use, planning, and memory.
- Evaluate, iterate, and optimize agent architectures to solve complex, multi-step enterprise workflows efficiently.
- Build and fine-tune document extraction pipelines, including (OCR systems, Layout-aware models, Vision-Language Models (VLMs), Multimodal document understanding and classification).
- Design scalable pipelines for enterprise document ingestion, enrichment, indexing, and retrieval.
- Build end-to-end AI pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
- Partner with platform and data engineering teams to productionize solutions on AWS or GCP.
- Monitor model performance, detect drift, and drive continuous improvement strategies.
- Design evaluation frameworks, offline metrics, and online experimentation (A/B testing) to measure real-world impact.
Requirements
- Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Data Science, or a related field.
- Strong proficiency in Python and modern ML frameworks (PyTorch preferred).
- Hands-on experience with applied research and translating research ideas into production-grade AI systems.
- Proven experience with knowledge graphs, graph embeddings, or graph neural networks.
- Experience building advanced RAG systems using vector databases and structured knowledge sources.
- Strong understanding of LLMs, embeddings, and fine-tuning techniques.
- Experience deploying AI systems in enterprise or large-scale production environments.
- A product-oriented, problem-solving mindset with the ability to quickly learn new domains and design efficient AI solutions under real-world constraints.
- Solid foundation in ML fundamentals, statistics, and experimentation.
Benefits
- 100% coverage for health, dental, and vision insurance for you and your dependents from day one.
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