Description

• 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

Key Skills

Education

Bachelor's degree