Staff Engineer - ML Infra / MLOps

Quince is a tech company disrupting the retail industry by putting AI, Analytics, and Automation at the center of everything they do. They are seeking a Staff Engineer for ML Infra / MLOps to design and implement scalable ML systems, optimize performance, and mentor junior engineers.

Responsibilities:

• Architect the ML Infrastructure Foundation: Own the end-to-end technical design of Quince’s ML platform — including model training, serving, feature pipelines, and monitoring — ensuring it is modular, scalable, and built for long-term extensibility
• Build the “Paved Road” for Production: Design and implement the core developer experience for Quince’s Data Scientists and AI Researchers, enabling them to move from “idea to production” with minimal friction and maximum reliability
• Drive Technical Excellence Across the Stack: Set and uphold engineering standards in CI/CD for ML, Infrastructure as Code (IaC), model versioning, experiment tracking, and deployment strategies (blue-green, canary) — and build the tooling that makes those standards the path of least resistance
• Own High-Impact System Design Decisions: Lead the technical evaluation and selection of core platform components — from inference runtimes and feature stores to orchestration frameworks — with a clear-eyed view of build vs. buy tradeoffs
• Optimize Compute Performance & Cost: Design and implement GPU utilization optimizations, model batching strategies, and cloud cost controls to maximize performance per dollar across training and inference workloads
• Ensure Production Scalability & Reliability: Architect ML serving infrastructure that gracefully handles traffic surges, seasonal spikes, and model version transitions, with robust monitoring, alerting, and automated recovery
• Mentor and Elevate the Engineering Team: Provide deep technical mentorship to junior and mid-level engineers through design reviews, code reviews, and pairing sessions — raising the collective technical bar without adding process overhead
• Champion Operational Excellence: Lead root-cause analyses (RCAs) for production failures and drive systemic, permanent fixes over reactive patches. Model a culture of rigorous on-call discipline and accountability