Senior Data Scientist With LoRA/QLoRA and Amazon Sagemaker

Role: Senior Data Scientist With LoRA/QLoRA and Amazon Sagemaker Location: USA Remote, FULL TIME Summary: Works on data to build, train and deploy next-generation LLM-based recommender systems. The role covers the full path from raw customer data to a deployed model. Responsibilities: Data to training sets: turn unstructured and structured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents. Feature and context engineering: decide what information goes into each document and how to express it, such as market-relative price, tier and trip context. Large-scale distributed training on Amazon SageMaker AI, including HyperPod. Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning. Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups. Inference and deployment: low-latency, cost-efficient serving, including quantization, distillation and profiling. Evaluation and error analysis: build evaluation sets, run ablations and catch data leakage. Write up findings clearly for the customer and the AWS science team. Data privacy: handle customer data safely, including removing per Required skills Spark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents. Amazon SageMaker AI: processing, training, distributed training (HyperPod), deployment. LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g. GRPO/PPO-style methods). Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training. Large-scale batch LLM inference and production inference (vLLM, Amazon Bedrock batch). Data privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation. Clear written communication with customer and science stakeholders. Preferred skills AWS Trainium / Inferentia (Neuron SDK). Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders. Embedding quantization or semantic IDs. Experience in a customer-facing or embedded role.