[Hiring] Staff ML Data Scientist @Robots and Pencils

Role Description

We’re looking for a top-tier ML Data Scientist to build, train, and deploy next-generation LLM-based recommender systems. You will work directly with a top-notch 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. We are looking for a seasoned applied scientist with a multi-year record of production ML, not someone who recently moved over from analytics or data science generalism.

What You’ll Do

• Turn structured and unstructured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents.
• Make the feature and context engineering decisions: what information goes into each document and how to express it (e.g., market-relative price, tier, trip context).
• Run large-scale distributed training on Amazon SageMaker AI, including HyperPod.
• Fine-tune models with LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.
• Own deep learning modeling, including architecture, ranking heads, and co-training and multi-task setups.
• Deploy low-latency, cost-efficient inference, including quantization, distillation, and profiling.
• Build evaluation sets, run ablations, catch data leakage, and write up findings clearly for Expedia and the AWS science team.
• Handle customer data safely, including personal-data removal, working inside the customer’s environment.
• Bring an AI-forward mindset to your daily work, using tools like Claude and Cursor to ship higher-quality work at pace.

Qualifications

• Spark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents.
• Hands-on Amazon SageMaker AI: processing, training, distributed training (HyperPod), and deployment.
• LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g., GRPO or PPO-style methods).
• Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training.
• Large-scale batch and production LLM inference (e.g., 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.
• Demonstrable usage of AI-forward tools such as Claude and Cursor.

Helpful Extras and Unique Skills

• AWS Trainium or Inferentia (Neuron SDK).
• Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders.
• Embedding quantization or semantic IDs.
• Customer-facing or embedded consulting experience.