Sr. Applied Scientist , Grocery, Retail & In-Store Experience (GRAISE)

We are looking for a Senior Applied Scientist who will own the science strategy and technical direction for computer vision and machine learning within the Amazon grocery ecosystem. You will identify the highest-impact problems in ambiguous rapidly evolving domains frame them rigorously and drive solutions from research through production at scale. You will lead cross-functional technical decisions with engineering product and business partners shaping not just what we build but also how we invest. This is a role where your scientific judgment architectural choices and ability to create clarity from ambiguity will directly define the intelligence layer powering millions of grocery shopping experiences.

Key job responsibilities
* Own the end-to-end science strategy for computer vision and machine learning solutions in the grocery domain navigating ambiguity to identify the highest-impact opportunities
* Develop novel approaches to complex unsolved perception and identification challenges where off-the-shelf methods are insufficient; publish findings internally or externally to advance the state of the art
* Define the evaluation framework and success criteria for model performance establishing metrics that connect scientific outcomes to measurable business impact and using these to influence roadmap prioritization
* Lead cross-functional technical design with engineering product and operations partners driving architecture decisions for model serving data pipelines and system reliability at scale rather than solely handing off models for productionization
* Identify and resolve ambiguous cross-team technical dependencies (e.g. upstream data quality annotation infrastructure model interoperability) that block progress across multiple workstreams; propose and drive solutions proactively
* Influence technical direction beyond the immediate team mentor scientists raise the bar in hiring establish best practices for experimentation and model development and represent the teams science strategy to senior leadership
* Communicate complex technical trade-offs and recommendations to VP-level stakeholders shaping investment decisions and aligning cross-org partners on science-informed product direction

A day in the life
As a Senior Applied Scientist on the GRAISE team youll own the technical strategy for how computer vision and multimodal learning come together to solve perception problems in grocery stores many of which no one has cleanly formulated yet. On any given day you might diagnose a surprising failure mode from overnight experiments and decide whether to pivot your approach entirely co-architect a serving system with engineers while defining confidence thresholds and graceful degradation paths present a precision-recall trade-off to senior leaders in terms that shape launch decisions and investment priorities or unblock a cross-team dependency on annotation infrastructure all while mentoring junior scientists and carving out time for deep technical work on problems the team hasnt cracked. Your scientific judgment and architectural choices will directly shape the shopping experience for millions of customers across Amazons grocery stores.

About the team
The GRAISE team (Grocery Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazons in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient economical personalized and enjoyable for customers while empowering retailers with operational efficiency

- 5 years of building machine learning models for business application experience
- PhD or Masters degree
- Experience programming in Java C Python or related language
- Experience with neural deep learning methods and machine learning

- Experience leading end-to-end science efforts from problem formulation through production launch
- Hands-on experience building training and deploying computer vision models in production systems operating at scale on real-world noisy data
- Experience building and improving continuous model training pipelines including human-in-the-loop annotation workflows active learning and data flywheel strategies that compound model quality over time
- Demonstrated ability to work with multimodal data (images video sensor signals text/catalog metadata) and design systems that fuse heterogeneous inputs for robust inference
- Familiarity with retail logistics or physical-world perception domains where environmental variability (lighting angles occlusion sensor diversity) makes controlled-lab performance an unreliable predictor of real-world accuracy

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