Oscar AI is building the first AI-Native Operating System for the frontline service economy. We power retailers, restaurants, convenience stores, and the thousands of teams who run America’s essential businesses. Our platform transforms fragmented operational data into clean intelligence, automated workflows, and production-grade AI copilots that help operators make faster, smarter decisions every day.

We’re now hiring a Founding ML/AI Engineer to shape the core architecture, data platform, and AI systems that will scale to thousands of locations. This is a high-ownership, deeply technical role where you will design, train, deploy, and operationalize the models at the heart of our product.

What you'll own:

AI Systems and Infrastructure

Architect and build end-to-end ML systems: data ingestion, feature pipelines, embeddings, model training, evaluation, and deployment.
Design scalable retrieval, summarization, and anomaly-detection pipelines that power Oscar’s AI copilots and insights engine.
Productionize models (LLMs, forecasting models, classifiers, rule-based hybrid systems) and build monitoring for drift, accuracy, latency, and reliability.

Data Platform and Core Architecture

Build the data infrastructure that supports multi-brand, multi-tenant ingestion from dozens of third-party systems (POS, back-office, IoT, etc.).
Define and refine our canonical data schemas, transformations, and storage layers to support robust AI use cases.
Partner with product and engineering to integrate model outputs into the product in a way that is explainable, reliable, and trustworthy.

Full-Stack Product Execution

Turn customer problems into shipped AI-powered features: ideate, architect, prototype, train, test, and deploy.
Work across the full stack when needed (React, Node/Nest, SQL, AWS) to deliver end-to-end solutions with minimal handoff.
Rapidly prototype new ML approaches, evaluate tradeoffs, and iterate based on real-world operator feedback.

Technical Leadership

Establish ML engineering standards, experimentation workflows, evaluation criteria, and MLOps best practices.
Make foundational decisions that will determine how our AI platform scales over the next decade.
Mentor others, raise the engineering bar, and help shape a culture of rigor, speed, and craftsmanship.

What Makes You a Great Fit:

5–10+ years hands-on engineering experience, with meaningful depth in ML/AI systems, distributed data pipelines, or large-scale backend architecture.
Strong fundamentals in computer science, data modeling, ML theory, and applied machine learning.
Experience owning production ML systems end-to-end (not just notebooks): deployment, monitoring, inference optimization, and iteration.
Comfortable jumping between backend, infra, ML models, and product integration.
Excited by ambiguity, zero-to-one building, and solving messy real-world data problems.
Clear communicator who collaborates well with product, engineering, design, and customers

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