ABOUT QUINCE
Founded in 2018, Quince was built to challenge the idea that nice things have to cost a lot. Our mission is simple: to make really high quality essentials for really low prices, produced fairly and sustainably. We believe everyone deserves exceptional craftsmanship and timeless design without the traditional markups. Quince is a direct-to-consumer (DTC) model that cuts out middlemen and leverages just-in-time manufacturing to minimize waste and maximize value. Quince is a tech company disrupting the retail industry by putting AI, analytics and automation at the center of everything we do. Our unwavering commitment to excellence and company values guide our teams and actions:
- Customer First: We prioritize customer satisfaction in every decision.
- High Quality: True quality means premium materials and rigorous production standards you can feel good about.
- Essential Design: We focus on timeless, functional essentials instead of chasing trends.
- Always a Better Deal: Innovation and transparency ensure value for both customers and partners.
- Social & Environmental Responsibility: We commit to sustainable materials, ethical production, and fair wages.
Quince partners with world-class manufacturers across the globe and serves millions of customers. With strong investor backing and a focus on sustainable growth, we are a company that is rapidly scaling while maintaining a commitment to quality, simplicity, and radical price transparency.
OUR TEAM AND SUCCESS
At Quince, you will be part of a high-performing team that is redefining what quality, value, and sustainability mean in modern retail. We are a destination for builders, innovators, and operators to come together and challenge the status quo. Our collective ambition is bold. We are creating an entirely new category and customer experience – one that democratizes luxury and provides high quality products at radically low prices. That mission demands a world-class team committed to excellence. If you are motivated by impact, growth, and purpose, you will find a strong sense of belonging at Quince.
THE ROLE
We are seeking a Senior Engineering Manager, MLOps to join our growing team. The ideal candidate is a technical visionary with a proven track record of building and scaling the underlying infrastructure that powers production-grade Machine Learning. You have a deep understanding of the ML lifecycle—from model development and distributed training to automated deployment and real-time monitoring—and you are passionate about treating infrastructure as a product for your "customers": Quince’s Data Scientists and AI Researchers. You are a self-starter who excels at identifying architectural bottlenecks and transforming them into seamless, automated "paved roads" that increase team velocity without sacrificing stability. Thriving in an environment of rapid growth and ambiguity, you make high-judgment decisions on "build vs. buy" and prioritize technical roadmaps that align directly with e-commerce business outcomes. Above all, you are energized by a culture of distributed decision-making and extreme candor, where you will lead a high-performing team to set new standards for how AI is industrialized at scale to serve Quince customers.
Responsibilities
- Define the MLOps Vision & Strategy: Architect a long-term roadmap that transitions ML workflows from manual scripts to a fully automated, self-service platform for all Quince Data Scientists and AI Researchers.
- Own the "Paved Road" for Production: Build and maintain the end-to-end infrastructure for model training, deployment, and serving, ensuring researchers can move from "idea to production" with zero friction.
- Drive Strategic Prioritization: Partner with business leaders to align infrastructure investments with core e-commerce drivers like real-time personalization, dynamic pricing, and inventory forecasting.
- Lead "Build vs. Buy" Evaluations: Make high-judgment decisions on when to leverage cloud-native services (e.g., SageMaker, Vertex AI) versus building custom internal tools to optimize for cost, speed, and flexibility.
- Guarantee System Scalability & Reliability: Oversee the uptime and performance of production ML services, ensuring the stack can handle massive traffic surges and seasonal spikes without degradation.
- Manage Compute Governance & Costs: Direct the optimization of high-cost computational resources, such as GPU clusters and cloud instances, balancing high-performance training needs with fiscal responsibility.
- Recruit and Mentor Top Talent: Build and lead a high-performing team of ML Infra and DevOps engineers, providing technical coaching, career pathing, and performance management.
- Establish MLOps Standards: Drive the adoption of best practices in CI/CD for ML, Infrastructure as Code (IaC), and automated testing to ensure a modular and maintainable system.
- Bridge the Research-Engineering Gap: Act as the primary cross-functional lead, translating the complex needs of AI Researchers into actionable engineering requirements for the infrastructure team.
- Define and Track Velocity Metrics: Establish KPIs for the infrastructure team, such as model deployment frequency, mean time to recovery (MTTR), and infrastructure cost per inference.
- Champion Operational Excellence: Lead root-cause analyses (RCAs) for production failures and foster a culture of accountability where systemic fixes are prioritized over "quick patches."
- Stay Ahead of the AI Curve: Monitor emerging trends in LLM-ops, vector databases, and real-time feature engineering to ensure Quince’s infrastructure remains competitive and future-proof.
Qualifications
Required:
- 10+ years of industry experience, with at least 3-5 years in a leadership or management role specifically focused on ML Infrastructure, MLOps, or large-scale Data Platform engineering.
- Proven track record of building and scaling MLOps platforms that support the full model lifecycle—from data ingestion and distributed training to real-time inference and monitoring.
- Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).
- Hands-on experience with ML frameworks and tooling, such as PyTorch, TensorFlow, Kubeflow, or SageMaker, and a strong opinion on how to integrate them into a cohesive developer experience.
- Expertise in building and managing Feature Stores and high-throughput data pipelines (using tools like Spark, Flink, or Kafka) to ensure data consistency across training and serving.
- Experience partnering with AI Research and Data Science teams to understand their unique workflows and translate research needs into robust, scalable engineering solutions.
- Strong understanding of CI/CD for ML, including automated testing for models, model versioning, and "blue-green" or "canary" deployment strategies.
- Demonstrated ability to manage high-cost compute resources, with experience optimizing GPU utilization and cloud spend in a hyper-growth environment.
- Excellence in operational leadership, with a history of driving service availability, performance, and stability through rigorous on-call rotations and root-cause analysis.
- A product-oriented mindset, with the ability to treat infrastructure as a platform and prioritize the roadmap based on researcher velocity and business ROI.
- Exceptional communication skills.