Staff ML Data Scientist

This is a fully remote position, open to applicants in Canada.

📋 Description

• Transform both structured and unstructured traveler and catalog data into narrative text documents and synthetic data on a scale of tens of millions of documents.

• Make decisions regarding feature and context engineering for document content and expression.

• Execute large-scale distributed training on Amazon SageMaker AI, including the use of HyperPod.

• Fine-tune models using LoRA/QLoRA, supervised training, and reinforcement learning focused on ranking and reasoning.

• Take ownership of deep learning modeling, which includes architecture design, ranking heads, co-training, and multi-task configurations.

• Implement low-latency, cost-effective inference strategies through quantization, distillation, and profiling.

• Create evaluation sets, conduct ablations, identify data leakage, and document findings for stakeholders at Expedia and AWS science.

• Manage customer data responsibly, ensuring personal data removal within the customer environment.

• Utilize AI-driven tools such as Claude and Cursor to deliver high-quality work efficiently.

• Collaborate with the science team, AWS account team, and AWS Applied Scientist.

⛳️ Requirements

• Experienced applied scientist with several years of production ML experience.

• Proficient in Spark (PySpark) and the Hadoop ecosystem (Hive, HDFS) with large corpora involving tens of millions of documents.

• Hands-on experience with Amazon SageMaker AI: processing, training, distributed training (HyperPod), and deployment.

• Expertise in LLM fine-tuning (LoRA/QLoRA) and reinforcement learning techniques (e.g., GRPO or PPO-style methods).

• Strong foundational knowledge in deep learning using PyTorch: architectures, training dynamics, and co-training.

• Experience with large-scale batch and production LLM inference (e.g., vLLM, Amazon Bedrock batch).

• Knowledge of data privacy and security on AWS, including personal data removal, IAM, KMS, and Lake Formation.

• Excellent written communication skills for engaging with customers and science stakeholders.

• Proven experience with AI-driven tools such as Claude and Cursor.

• Additional skills that are beneficial: familiarity with AWS Trainium or Inferentia (Neuron SDK); experience with recommender systems and ranking metrics (NDCG, MRR); knowledge of embedding quantization or semantic IDs; customer-facing or embedded consulting experience.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Opportunities for professional development and continued education.

• Flexible work hours and remote work options.

• A collaborative, innovative work environment with a focus on cutting-edge technology.