Senior Staff Applied ML Engineer

Role Description

The Consumer Agent team builds Shopify's consumer-facing agentic shopping experiences — the AI agents inside the Shop app and on merchant storefronts that help millions of buyers search, discover, and buy. As a Senior Staff Applied Machine Learning Engineer, you'll be the person we point at the hardest problems on the surface and trust to drive it to a shipped, measured outcome.

• Own ambiguous, high-leverage problems end to end.
• Raise the bar for evaluation across the team.
• Ship weekly on a fast-moving consumer surface.
• Work at the intersection of LLM orchestration, model training and distillation, search and retrieval, and cost/latency engineering.
• Partner with frontier model labs and ML teams across Shopify.

Key Responsibilities

• Build and improve student models through supervised fine-tuning and knowledge distillation.
• Design and raise the bar for ML evaluation across the team.
• Develop agentic memory and personalization signals.
• Work on whole-page optimization: page composition, ranking, and blending for the agent results surface.
• Build propensity and predictive models for intent and purchase propensity across agent surfaces.
• Move fluidly between distillation, memory, ranking, propensity, and evaluation as priorities shift.
• Apply rigorous experimentation on a high-traffic surface.
• Integrate cleanly with an LLM orchestration stack already in flight.
• Become the technical reference point for the team.
• Contribute to cross-team ML architecture conversations.
• Ship weekly, absorbing ambiguity rather than adding coordination overhead.

Qualifications

• You've shipped LLM systems to production at real scale.
• Hands-on depth in model training and distillation.
• Comfort with the serving and throughput-economics side of inference.
• Rigorous evaluation methodology.
• Operate as a senior individual contributor with high autonomy.
• Breadth over a single specialty.
• Strong communication and collaboration in a fast-paced, cross-functional environment.
• Able to work with significant overlap with North America time zones.

Nice to have

• E-commerce, marketplace, commerce-search, recsys, or ads background.
• Inference and serving internals knowledge.
• Reinforcement learning experience.
• Propensity, ranking, or classical applied ML at scale experience.
• Counterfactual evaluation experience.
• Experience working alongside or with frontier AI products and agentic systems.