Fulltime :: Senior Data Scientist / ML Engineer :: San Jose, CA (Onsite)

Summary Works directly with Client and its data to build, train and deploy next-generation LLM-based recommender systems. Works closely with the client's science team, the AWS account team and the AWS Applied Scientist leading the science. The role covers the full path from raw customer data to a deployed model.

Responsibilities:

1. 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.
2. 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.
3. Large-scale distributed training on Amazon SageMaker AI, including HyperPod.
4. Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.
5. Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups.
6. Inference and deployment: low-latency, cost-efficient serving, including quantization, distillation and profiling.
7. 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.
8. 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