Staff ML Data Scientist

🔥 0 minutes ago

🇨🇦 Canada – Remote

⏰ Full Time

🔴 Lead

📊 Data Scientist

👻 Ghost score 15%

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Robots & Pencils

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤖 Artificial Intelligence

🤝 B2B

☁️ SaaS

💰 Venture Round on 2022-04

Artificial Intelligence • B2B • SaaS

Robots & Pencils is a digital innovation firm that specializes in developing digital strategies and products utilizing mobile, web, and frontier technologies to drive transformation for businesses. They focus on areas such as mobile development, product design, UX research, artificial intelligence, machine learning, and organizational change management. With a commitment to blending creativity with technology, they aim to unlock data insights and enhance product innovation, ultimately delivering excellent customer experiences and helping brands gain a competitive edge.

📋 Description

• 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 feature and context engineering decisions for document content and expression • Run large-scale distributed training on Amazon SageMaker AI, including HyperPod • Fine-tune models with LoRA/QLoRA, supervised training, and reinforcement learning for ranking and reasoning • Own deep learning modeling, including architecture, ranking heads, co-training, and multi-task setups • Deploy low-latency, cost-efficient inference using quantization, distillation, and profiling • Build evaluation sets, run ablations, detect data leakage, and write findings for Expedia and AWS science stakeholders • Handle customer data safely, including personal-data removal within the customer environment • Use AI-forward tools such as Claude and Cursor to ship high-quality work at pace • Collaborate with the science team, AWS account team, and AWS Applied Scientist

🎯 Requirements

• Seasoned applied scientist with a multi-year record of production ML • 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: AWS Trainium or Inferentia (Neuron SDK); recommender systems and ranking metrics (NDCG, MRR); embedding quantization or semantic IDs; customer-facing or embedded consulting experience

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