Generative AI Applied Scientist (Search, Retrieval & Ranking)

Quick Facts

• Role: Generative AI Applied Scientist (Search, Retrieval & Ranking)

Description

Build production-ready generative AI solutions for large-scale search, retrieval, and ranking. You will train and fine-tune retrieval models, fine-tune and adapt LLMs, design training-data pipelines, and develop automated evaluation frameworks. You will also improve inference efficiency and scale training and high-throughput inference on GPU clusters while collaborating with engineers and product teams.

Responsibilities

• Train and fine-tune retrieval models (dense embeddings, late-interaction multi-vector models, cross-encoder rerankers), with and without instruction conditioning

• Fine-tune and adapt LLMs, including small efficient models, using LoRA/PEFT, distillation, and preference optimization (RLHF, DPO)

• Build training-data pipelines (contrastive pairs, hard-negative mining, LLM-generated synthetic data, and data-quality analysis)

• Develop evaluation frameworks to measure retrieval/ranking quality (nDCG, recall, MRR) and model behavior with automated, reproducible pipelines

• Improve efficiency using model-internal analysis and techniques such as quantization, pruning, and distillation

• Scale distributed training and high-throughput inference on GPU clusters with production serving support

• Collaborate cross-functionally from concept to launch and present results to technical and leadership audiences

Requirements

• PhD in Computer Science, Machine Learning, or related field, or MS with 5+ years of applied ML research experience

• Strong deep learning and NLP fundamentals with hands-on transformer training/fine-tuning

• Proficiency in PyTorch and strong Python programming skills

• Experience with post-training, fine-tuning, or representation learning for large models

• Experience designing rigorous experiments and evaluations and analyzing model behavior

• Demonstrated ability to turn research ideas into working systems

Benefits

• Work on hard generative AI and retrieval problems with a focus on privacy, security, accuracy, efficiency, and robustness

• Collaborative environment with cross-functional work from concept to launch