{"id":4194898,"url":"https://alion.io/job/robots-pencils-staff-ml-data-scientist","title":"Staff ML Data Scientist","company":{"id":7662,"name":"Robots & Pencils","domain":"robotsandpencils.com","url":"https://alion.io/company/robots-and-pencils","size_band":"201-500","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":82,"open_postings":7,"ghost_share":0,"stale_share":0.714,"repost_share":0,"time_to_fill_p50_days":32,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Canada"],"countries":["CA"],"hiring_countries":["CA"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Trainium","optional":false},{"name":"Claude","optional":false},{"name":"Context Engineering","optional":false},{"name":"Cursor","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GRPO","optional":false},{"name":"Hadoop","optional":false},{"name":"IAM","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Model Distillation","optional":false},{"name":"PPO","optional":false},{"name":"pySpark","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"Quantization","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Spark","optional":false},{"name":"Synthetic Data","optional":false},{"name":"vLLM","optional":false},{"name":"PEFT","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-11-12T19:45:18Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T20:20:17Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Staff ML Data Scientist, LLM Recommender Systems \nWe’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.\nWhy This Role Matters\nAt 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.\nWhat You’ll Do\nTurn structured and unstructured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents\nMake 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)\nRun large-scale distributed training on Amazon SageMaker AI, including HyperPod\nFine-tune models with LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning\nOwn deep learning modeling, including architecture, ranking heads, and co-training and multi-task setups\nDeploy low-latency, cost-efficient inference, including quantization, distillation, and profiling\nBuild evaluation sets, run ablations, catch data leakage, and write up findings clearly for Expedia and the AWS science team\nHandle customer data safely, including personal-data removal, working inside the customer’s environment\nBring an AI-forward mindset to your daily work, using tools like Claude and Cursor to ship higher-quality work at pace\nWhat You’ll Bring\nSpark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents\nHands-on Amazon SageMaker AI: processing, training, distributed training (HyperPod), and deployment\nLLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g., GRPO or PPO-style methods)\nStrong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training\nLarge-scale batch and production LLM inference (e.g., vLLM, Amazon Bedrock batch)\nData privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation\nClear written communication with customer and science stakeholders\nDemonstrable usage of AI-forward tools such as Claude and Cursor\nHelpful Extras and Unique Skills\nAWS Trainium or Inferentia (Neuron SDK)\nRecommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders\nEmbedding quantization or semantic IDs\nCustomer-facing or embedded consulting experience","description_format":"text","description_chars":3111,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["UI/UX Design","Custom Software Development","AI Consulting & Integration"],"lifecycle":[{"event":"open","at":"2026-11-12T20:20:17Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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