Staff ML Data Scientist

Why This Role Matters

At Robots & Pencils, we design AI systems for a human world. Our name says it all. Robots and pencils means engineering paired with creativity, because every system we ship has to work for real people in real workflows. Here, you shape how AI integrates into enterprise operations and how products create measurable impact for clients and the people they serve. We ship production-ready AI in 30 to 45 days. That pace demands people who take ownership, lead with craft, and care deeply about what they put their name on.

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

What You’ll Bring

* 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